Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 329 appraisals
Journal of psychiatric research
Systematic review of machine learning and deep learning models for EEG-based detection of depression.
OBJECTIVE: Depression is a leading cause of global disability, motivating the development of objective and scalable diagnostic approaches. Quantitative electroencephalography (QEEG) combined with machine learning (ML) and deep learning (DL) techniques has gained increasing attention for depression detection. This systematic review aimed to critically examine and descriptively compare the methodologies, performance metrics, and limitations of ML- and DL-based models applied to EEG data for depression detection. METHODS: A systematic review was conducted in accordance with PRISMA 2020 guidelines. Seven electronic databases (PubMed, Scopus, IEEE Xplore, ScienceDirect, Web of Science, SAGE Journals, and MDPI) were searched for peer-reviewed studies published between 2020 and 2024. Eligible studies included human participants, used EEG signals for depression detection, and applied ML or DL algorithms. Extracted information comprised algorithm type, sample size, EEG acquisition parameters, validation strategies, and reported performance metrics, which were synthesized descriptively across studies. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. RESULTS: A total of 42 studies met the inclusion criteria, including 23 ML-based and 19 DL-based investigations. Reported classification accuracy ranged from approximately 76% to 100%. DL studies showed a higher mean reported accuracy than ML studies (93.92% vs. 90.78%); however, this difference was not statistically significant in the exploratory non-parametric comparison. Near-perfect performance values were frequently observed in studies with small sample sizes and subject-dependent or exclusively internal validation strategies, raising concerns regarding overfitting and limited generalizability. Studies relying on publicly available datasets tended to report more stable performance. QUADAS-2 assessment revealed recurrent risk-of-bias concerns, particularly in the domains of patient selection and index test conduct. CONCLUSIONS: Both ML and DL approaches demonstrate potential for EEG-based depression detection, but reported performance differences between them should be interpreted cautiously. Although DL studies tended to report higher accuracy values, this pattern was not statistically significant in exploratory analyses and was strongly influenced by sample size, validation strategy, and methodological design. Future research should prioritize larger and more diverse samples, subject-independent or external validation strategies, and standardized reporting frameworks to enhance methodological rigor and clinical applicability.
2 Aug 2026
Read appraisal →Sleep medicine reviews
Artificial intelligence in sleep medicine I: Diagnosis, treatment, care, and research
Artificial intelligence (AI) is transforming sleep medicine (SM) through improved diagnostics, therapeutics, and research. AI enhances diagnostic accuracy, treatment personalization, workflow efficiency. However, implementation requires careful validation and oversight. This review explores AI applications across the sleep disorders spectrum. AI in diagnostic SM covers applications including polysomnography and home sleep testing. We examine how AI detects early neurodegenerative changes in REM sleep behavior disorder, identify novel biomarkers, and optimize chronotherapeutic timing. Wearables complement these advances by continuously monitoring sleep patterns, movement and physiological signals in natural settings, generating rich datasets ideal for AI analysis. In therapeutics, machine learning models enhance sleep apnea phenotyping, enabling precise treatment selection, and improve CPAP adherence prediction. AI helps identify biomarkers to optimize the timing of chronotherapeutic interventions. In medical care, natural language processing facilitates unstructured clinical chart data extraction. Implementation challenges include data standardization, algorithmic bias, and the "generalization gap." We provide a structured framework for clinical implementation, emphasizing validation requirements and ethical considerations. Large datasets using routine clinical care, cohort studies, and wearable derived data create vast amounts of data. In return, AI tools and methodologies enhance and expand development of phenotypes and endotypes with the final goal of personalized and precision SM.
2 Aug 2026
Read appraisal →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 →BMC ophthalmology
The effect of lithium on the structure and function of the human retina: a systematic review
BACKGROUND: Bipolar disorder and depression are associated with structural and functional changes in the retina, including a thinner retinal nerve fibre layer (RNFL). Lithium is widely considered the most effective treatment for bipolar disorder, but its mechanism of action is not fully understood. We assessed research looking at the effect of lithium on structural or functional retinal outcomes in humans. METHODS: Searches using the terms 'Lithium' AND 'retina' were carried out to identify peer reviewed studies assessing the impact of lithium on retinal structure or function. These included those with or without a control group comparison, pre- and post- lithium comparisons and observational studies. There were no exclusions based on the quantity or preparation of lithium administered, or the length of administration. Risk of bias was assessed using the Joanna Briggs Institute (JBI) critical appraisal tool for Analytical Cross Sectional Studies, and a narrative synthesis and tabulated summary of the included studies was completed. RESULTS: Seven studies assessing structural outcomes and 10 reporting functional ones were identified, all highly heterogenous and with multiple limitations. Structural outcomes were derived exclusively from optical coherence tomography (OCT) with retinal nerve fibre layer (RNFL) being the most common measurement. There was no evidence of differences in the RNFL between participants with bipolar disorder taking lithium and healthy controls in two larger studies. In six studies looking at differences in those with bipolar disorder taking lithium and those taking valproate, two showed no signs of difference and four showed evidence of thicker RNFL in the lithium group. Studies reporting functional outcomes reported a statistically significant effect of lithium on at least one functional measure, derived from electrooculography, electroretinography, and dark adaptation thresholds. CONCLUSIONS: Current evidence suggests that lithium is likely to have an effect on the retina but limitations in all studies mean better designed and adequately powered prospective studies are required. REGISTRATION: PROSPERO database (Number-CRD42024516635).
31 July 2026
Read appraisal →The Spanish journal of psychology
A Systematic Review of the Risk and Protective Factors for Suicide in Autism Spectrum Disorder
The shorter lifespan and increased prevalence of physical and psychiatric comorbidities in autistic people is a challenge for mental health. There is a high risk of suicide-related behaviors in autism, which are related to mental health issues, social isolation, and other factors. Here we aimed to systematically review the evidence on risk and protective factors of suicidal behaviors in autistic people. Electronic datasets (PubMed, Scopus, Web of Science, and PsycInfo) were searched for empirical articles on the risk and protective factors for suicide in autism. The PRISMA guidelines were followed, and a series of inclusion and exclusion criteria were applied to the retrieved data. This review included 19 studies reporting multiple risk factors associated with suicide-related behaviors. Noteworthy contributors among these factors include psychiatric comorbidities, bullying, cyberbullying, teacher harassment, and cognitive functions. The evidence for protective factors is limited; however, social relationships are highlighted. Identifying risk and protective factors is paramount for detecting early warning signs and enabling the prevention and intervention of suicidal behavior. Our review identified key sociodemographic, clinical, and cognitive risk factors to take into account as alarm-signs when identifying suicidal behaviors in autism.
31 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Suicide in neurodegenerative diseases: a systematic review.
BACKGROUND AND OBJECTIVE: Suicide is a public health issue, which differs from suicidality, the continuum from suicidal ideation to the suicidal act, including suicide attempts and completed suicide. The main goal of the present study is to determine the relationship between Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and multiple sclerosis (MS) with suicidality. METHODS: This is a systematic review aiming to determine the relationship between AD, PD, ALS, and MS with suicidality following PRISMA 2020 guidelines by collecting cross-sectional, case-control, and cohort studies; case series; case reports; and retrospective and prospective studies from Google Scholar, PubMed, and Cochrane Library. The protocol of this systematic review was registered on PROSPERO; the registration number is CRD420261422354. RESULTS: From 2247 records identified from electronic databases, only 24 articles were included: three studies focusing on AD, nine on PD, and six studies focusing on ALS and MS, respectively. These studies exhibited moderate to low risk of bias. Despite the broad differences regarding the neurochemistry, pathophysiology, diagnosis, symptoms, and treatments of the selected diseases, patients are at a higher risk of suicidality. Depression and low social connectivity are the most prevalent risk factors. Suicidality is mainly detected during the first years post-diagnosis in PD, ALS, and MS patients, while the results in AD are confusing. CONCLUSIONS: Data about this topic is scarce and largely varying. Further research is required to elucidate this paradigmatic realm, fostering awareness, enhancing therapies, and providing explanations and interpretations of the mechanisms involved.
30 July 2026
Read appraisal →Neuropsychologia
Reading between the lines: Combining pause dynamics and semantic coherence for automated assessment of thought disorder.
Formal thought disorder (FTD), a hallmark of schizophrenia spectrum disorders, manifests as incoherent speech and poses challenges for clinical assessment. Traditional clinical rating scales, though validated, are resource-intensive and lack scalability. Automated speech recognition (ASR) allows for objective quantification of linguistic and temporal features of speech, offering scalable alternatives. Furthermore, ASR-derived utterance timestamps provide access to pause dynamics, which are thought to reflect the cognitive processes underlying speech production. Yet, their added value beyond semantic measures remains insufficiently explored. In this study, we evaluated a scalable multimodal framework that integrates pause features with semantic coherence metrics across three datasets: naturalistic self-recorded diaries (AVH, n = 140 participants), structured picture descriptions (TOPSY, n = 72 participants), and dream narratives (PsyCL, n = 43 participants). Pause-related features were evaluated alongside established coherence measures using support vector regression (SVR) to predict clinical FTD scores. Models using pause features alone robustly predict manually rated FTD severity consistently across datasets. Integrating pause features with semantic coherence metrics enhanced predictive performance compared to coherence-only models, with late fusion yielding the most robust and consistent gains in all three datasets. On average across datasets, Spearman correlation increased from ρ = 0.413 for semantic-only models to ρ = 0.455 with late fusion. The performance gains from semantic and pause features integration held consistently across all contexts, though the nature of the most informative pause patterns was dataset-dependent. These findings suggest that both pause dynamics and semantic coherence reflect complementary aspects of thought disorganization. Our integration framework provides a scalable approach for refining the assessment of disorganized speech to advance automated speech analysis in psychosis.
29 July 2026
Read appraisal →Neuropsychologia
Number of types of synaesthesia can be predicted by structural and functional neuroimaging data.
It has been suggested that synaesthetes have a distinct neurocognitive profile with a broad variety of cognitive and behavioural differences. Recent studies have shown that people with more types (relative to fewer types) of synaesthesia are easier to classify using machine learning of questionnaires and cognitive test data. This suggests a spectrum within synaesthesia, despite synaesthesia itself being typically defined in categorical terms. This study uses the same basic approach applied to 13 brain-based biomarkers. These have previously been shown to distinguish synaesthetes from controls, but it is not known whether they explain heterogeneity amongst synaesthetes. Using machine learning methods (elastic net regression), we were able to find several biomarkers that predict above chance the number of types of synaesthesia. These include both functional MRI (the extent to which brain regions act as hubs) and structural MRI (e.g., intracortical myelination) measures. This is the first project that explores whether it's possible to predict the breadth of synaesthesia from brain-based measures.
29 July 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Active linking through narratives facilitates associative inference
In daily life, we often draw inferences about novel associations from prior experiences. This ability, associative inference, is thought to be a key process by which memory guides future behavior. Prior studies have typically instructed participants to use narratives or mental imagery to aid memory of object associations. However, these studies have not explicitly tested the role of narratives, leaving an important question: how crucial are narratives in associative inference, and what role might they play? We used a modified associative inference paradigm to answer these questions. Our design factorially combined two factors during associative encoding: use of narratives, and explicitly linking the two objects. Participants separately learned AB and BC object pairs within ABC triads using four sets of instructions: i) generate narratives to link objects together, ii) generate narratives for objects separately, iii) compare perceptual properties of two objects, and iv) describe perceptual properties of objects individually. We tested participants' ability to infer AC associations as a function of encoding strategy. Across two experiments, using narratives to actively link objects produced a robust associative memory advantage that persisted across 1-day delay and remained substantial under more stringent retrieval conditions. This benefit was not attributable to a simple linking effect or to surface-level linguistic similarity between stories. A follow-up experiment revealed that this benefit did not generalize to perceptual memory for individual objects; instead, perceptual encoding facilitated perceptual object memory. These results reveal that deep, extended encoding emphasizing either narrativity or perceptual processing can benefit different aspects of retrieval.
28 July 2026
Read appraisal →Cerebellum (London, England)
Advancing Cerebro-cerebellar Network Imaging with Ultra-high Field (7T) MRI: Progress and Future Challenges
Ultra-high field (UHF) MRI (≥ 7T) is increasingly used to study the cerebro-cerebellar networks, offering substantial gains in signal-to-noise ratio and blood oxygen level dependent (BOLD) sensitivity that enable submillimeter functional and structural imaging. These advantages are particularly relevant for the cerebellum, whose tightly folded cortex and small deep nuclei have historically been difficult to resolve in vivo. In this targeted review, we synthesize recent progress in UHF-MRI applied to cerebro-cerebellar network mapping and critically examine the methodological challenges that accompany these developments. We highlight how UHF-MRI has enabled more precise delineation of cerebellar functional territories, improved visualization of the dentate nucleus and its connectivity, and facilitated integration of cerebellar nodes into whole-brain network models. We also discuss the technical challenges, including RF-field inhomogeneity, susceptibility-induced distortions, and the trade-off between spatial resolution and signal strength. These issues are compounded in the cerebellum due to its anatomical location and fine-scale organization, increasing the risk of spatial biases and misinterpretation if not carefully addressed. Beyond acquisition, we identify key gaps in analysis methodologies, including the need for cerebellum-specific preprocessing pipelines, segmentation tools that preserve mesoscale anatomy, and frameworks that treat the cerebellum and cerebrum as a unified system. We also discuss emerging opportunities, such as neurostimulation, integration with postmortem imaging and deep learning-based methods that may help bridge microstructural and in vivo findings. Overall, we argue that while UHF-MRI holds transformative potential for cerebellar systems neuroscience, translational progress will depend on rigorous methodological standardization and awareness of its limitations.
27 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 →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
The past and future of neuropsychological assessment: Back to the origins to face digital technological changes.
BACKGROUND: The procedures used for the neuropsychological assessment of patients with dementia have been undergoing significant transformations relating to technological developments and the diffusion of artificial intelligence tools. OBJECTIVES: The integration of a theoretical position and a practical approach is needed in clinical contexts so that newly available tools can enhance the fundamental principles underlying neuropsychological assessment. METHODS: Following a historical review of the various approaches to neuropsychological assessment, an analysis of the risks associated with the introduction of digital techniques, when not supervised by expert clinicians, is presented. The need to integrate new technologies in a comprehensive assessment involving clinical observation, ad-hoc tests, and other sources of information such as neuroimaging and neurophysiological data is discussed. RESULTS: The integration of new technologies with a clinical, personalised approach to the patient represents a challenge for neuropsychology that deserves ad-hoc research and discussion.
26 July 2026
Read appraisal →Journal of neural engineering
Multimodal quantification of cognitive load using a printed wearable facial bio-potential system
Objective.Cognitive load refers to the amount of mental effort required to process information and perform tasks. It has a strong impact on both learning and task performance and, therefore, plays a central role in cognitive neuroscience, psychology, neurology, human-machine interaction, and education research. Despite its significance, measuring cognitive load is challenging due to its complex and dynamic nature, influenced by task complexity, individual differences, and emotional states. While standard methods are based on subjective self-reports, numerous objective methods have been explored to assess cognitive load, such as electroencephalography (EEG), heart rate variability, and performance-based metrics. Despite these efforts, it remains challenging to objectively measure cognitive load in stationary and even more so in freely moving individuals.Approach.In this study, a wireless wearable system was employed to collect facial bio-potential markers and systematically compare their sensitivity to cognitive load. A dedicated task paradigm was developed and validated to induce graded cognitive load, allowing precise synchronization of physiological signals with task events. Facial muscle activity, eye movements, and brain activity were acquired simultaneously using a single facial electrode array.Main results.Across nine participants, entropy-based features tracked cognitive load more closely than conventional band-power measures. Facial EMG was the most informative modality: a cross-subject (leave-one-subject-out) model predicted the NASA-TLX weighted workload score with a mean Spearman correlation= 0.792 (95% CI [0.747, 0.836]; mean absolute error4.3, on a NASA-TLX 0-100 scale), and a single forehead-EMG entropy feature (the EMG-band entropy of forehead channel Ch14, above the right eyebrow) was selected in every cross-subject fold, generalizing across all participants without subject-specific calibration. An EEG-only model carried weaker but genuine cross-subject information (= 0.739), whereas EOG features did not pass the feature-selection threshold in this seated paradigm.Significance.Facial bio-potentials offer a practical, wearable system for objective cognitive load assessment. An outdoor pilot experiment further demonstrated electrophysiological measures under non-laboratory conditions. These findings offer insights into the suitability of different physiological indicators for assessing cognitive load, particularly the informativeness of facial EMG for seated assessment and the robustness of blink-based measures under ambulatory motion, contributing to the future development of practical measurement techniques in dynamic everyday settings.
23 July 2026
Read appraisal →Current pain and headache reports
Chronic Pain and the Risk of Dementia: A Systematic Review and Meta-Analysis
BACKGROUND: The relationship between chronic pain and dementia risk has been extensively studied, but findings remain inconclusive. We conducted an updated meta-analysis to synthesize evidence from recent large-scale cohort studies. METHODS: Two authors independently and systematically searched PubMed, Web of Science, Embase, Cochrane Library, and Chinese National Knowledge Infrastructure for cohort studies published through April 2025, with a minimum follow-up of one year. Hazard ratios (HRs) were considered equivalent to risk ratios (RRs) assuming low event rates. Random-effect models pooled risk ratios (RR) with 95% confidence intervals (CI). Subgroup analyses and meta-regression explored heterogeneity. RESULTS: Of 3,823 publications, 30 studies met the inclusion criteria. After excluding five studies with overlapping populations, 25 studies involving 2,091,835 participants were pooled in the primary analysis. Individuals with chronic pain had a 28% higher dementia risk (RR = 1.28, 95% CI = 1.18-1.38). Heterogeneity was high (I²=91%). Subgroup analyses showed migraine, headaches, arthritis-related pain, and widespread pain were associated with increased risk. The association was significant in Asia but not in America or Europe, and when using ICD (but not DSM) criteria. Chronic pain increased the risk of Alzheimer's disease and non-vascular dementia, but not vascular dementia. Study quality, region, measurement methods, and pain types influenced the relationship. Pain type was a source of heterogeneity. CONCLUSIONS: In this meta-analysis of cohort studies, chronic pain was associated with an increased risk of dementia. Due to the observational nature of the included studies and high heterogeneity, this association should not be interpreted as causal. Further research is needed to determine whether pain management can mitigate dementia risk.
23 July 2026
Read appraisal →Biomedical physics & engineering express
MELF: a multi-view ensemble learning framework for normative resting state EEG signal quality assessment
Normative resting-state electroencephalography (rsEEG), defined as standardized recordings from healthy populations, provides essential baselines for studying brain mechanisms and psychiatric disorders. However, its reliability critically depends on rigorous quality assessment, as artifacts or over-processing can distort subsequent analyses. Current approaches often rely on single-feature perspectives, which fail to capture the multidimensional nature of signal quality. We propose MELF, a quality assessment framework for normative rsEEG that systematically integrates three complementary feature domains: time domain (mobility, complexity), frequency domain (-band relative power, parallel log spectra index (PaLOSi)), and spatial domain (-band directed phase lag index, bad channel ratio, ICLabel-classified brain component proportion). A random forest-based ensemble classifier is constructed using dynamic weight allocation based on cross-validation accuracy (Acc). Experiments on four open rsEEG datasets (DSTR, TRCS, LEMON, and HBN) and expert-annotated clinical data demonstrate that: (1) MELF achieves up to 10% Acc improvement over single-view methods; (2) MELF outperforms traditional machine learning and deep learning baselines while reducing computational time, reaching 97.69% Acc (AUC = 0.9957), and maintains strong performance on an independent test set (92% Acc, AUC = 0.88); (3) the spectral view contributes the most, confirming the evaluative capability of the PaLOSi. MELF provides an interpretable, high-Acc, and robust automated framework for normative rsEEG quality assessment, with broad applications in neuroscience research and clinical quality control.
22 July 2026
Read appraisal →JMIR mental health
Optimizing Treatment Strategies in the Bipolar Disorder Spectrum With Classical AI Approaches: Systematic Review of Performance, Bias, and Clinical Applicability
BACKGROUND: Bipolar disorder (BD) is a complex and heterogeneous psychiatric condition, characterized by fluctuating clinical courses that affect approximately 1%-2% of the global population in their lifetime. Despite pharmacological advances, treatment response varies significantly among patients, making the identification of individualized treatment strategies a major challenge. Artificial Intelligence (AI), through its classical approaches, has emerged as a powerful tool in precision psychiatry to identify subtle patterns in complex data and inform personalized clinical decisions. OBJECTIVE: The present systematic review aimed to examine the current evidence on classical AI-supported treatment optimization in the BD spectrum. METHODS: The review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Four databases (PubMed, Web of Science, Scopus, and Embase) were searched for original studies published after 2015 on the application of classical AI in the treatment of BD in adult patients. Publication bias was evaluated by visual inspection of a funnel plot. The methodological quality, risk of bias, and clinical applicability of the predictive models were assessed using the Prediction Model Risk Of Bias Assessment Tool for prediction models using regression or AI methods (PROBAST+AI; PROBAST+AI Working Group) tool. RESULTS: A total of 35 studies were included and classified into 5 outcome-based categories, including acute symptomatic response, long-term maintenance response, relapse and readmission risk, safety and dose optimization, and brain aging and phenotyping. Acute symptomatic response models performed modestly (pooled area under the curve [AUC] 0.68), while imaging improved accuracy (74%-77%). Long-term maintenance response models showed moderate-to-high performance (pooled AUC 0.80), with biomarker- and cellular-based models reaching 96%-99% accuracy. Relapse and readmission prediction achieved a pooled AUC of 0.71, with digital phenotyping and rule-based methods performing best (AUC 0.85-0.88). Safety and dose optimization models achieved 85%-97% accuracy. Brain aging and phenotyping studies highlighted accelerated brain aging in BD, partially mitigated by lithium, and revealed novel data-driven subgroups. However, 3 studies were considered at high risk of bias due to small sample sizes associated with disproportionately high-performance estimates. An additional study was identified as potentially biased because it lay markedly distant from the funnel plot's confidence line. Finally, the PROBAST+AI assessment revealed a high risk of bias in most studies, primarily due to data analysis limitations, small sample sizes, and lack of external validation. CONCLUSIONS: The adoption of classical AI tools in BD serves as a driver for therapeutic optimization, although current AI tools in BD should still be considered exploratory rather than ready for clinical use. Effective implementation in real-world clinical scenarios requires more robust, transparent, and externally validated models to ensure reliability and generalizability.
22 July 2026
Read appraisal →Journal of neural engineering
Dynamic functional graph-Laplacian priors integrated with optimization for EEG source localization
Objective. Electroencephalography (EEG) source localization is an ill-posed inverse problem in which conventional methods often rely on static anatomical or smoothness assumptions and may neglect task-related dynamic functional interactions. This study aims to develop a dynamic graph-regularized EEG source localization framework that incorporates time-varying functional connectivity directly into inverse reconstruction and improves source-space motor imagery (MI) decoding.Approach. We propose DynaGraph-alternating direction method of multipliers (DG-ADMM), a source localization framework that combines linearly constrained minimum variance (LCMV) beamforming, region-of-interest-level dimensionality reduction, dynamic phase synchronization analysis, graph-Laplacian regularization, and efficient optimization. Initial source estimates are obtained using a LCMV beamformer. Region-level source signals are then extracted using principal component analysis and sliding-window phase-locking values with surrogate-based statistical testing are used to construct reliable dynamic functional graphs. The resulting graph Laplacian is mapped back to source space and embedded as a structured prior in an ADMMs optimization problem.Main results. Experiments on the MNE sample dataset showed that DG-ADMM produced spatially concentrated and physiologically plausible source patterns. On the PhysioNet MI dataset, the proposed framework achieved a binary left-versus-right MI classification accuracy of 93.52%, outperforming representative deep learning baselines. In a 320-dataset synthetic benchmark covering single-source, double-source, correlated-source, and dynamic-source conditions at two signal-to-noise ratios, DG-ADMM achieved the lowest mean center-of-mass localization error in five of eight conditions and showed its clearest advantage for dynamic sources.Significance. The results demonstrate that dynamic functional connectivity can serve as an informative graph-structured prior for EEG inverse reconstruction. DG-ADMM provides an interpretable and computationally feasible strategy for improving spatial focus, temporal consistency, and source-space decoding performance in EEG-based brain-computer interfaces.
21 July 2026
Read appraisal →Neuroinformatics
NeuroFusion: A Unified Framework for Generalized Visual Stimulus Decoding from fMRI Across Datasets and Subjects
Recent advancements in neural decoding have shown promising results in reconstructing visual experiences from brain activity. However, existing approaches focus primarily on decoding within a single dataset or subject, which limits generalization across various sources of neuroimaging. In this work, we propose a novel framework for the decoding of visual stimuli between subjects and between data sets, integrating neural recordings from multiple publicly available fMRI datasets. To address inherent intersubject and interdataset variability, we introduce a contrastive learning-based alignment strategy using image embeddings from a pre-trained IP-Adapter model. Our approach learns a shared latent space by aligning subject-specific neural representations with image features, enabling generalized decoding across both subjects and datasets. In addition, we propose a simple yet effective data augmentation method using ridge regression. This method synthesizes realistic fMRI-like signals from novel images by predicting voxel activity and injecting learned noise distributions, thus enhancing training diversity and model robustness. To the best of our knowledge, while several recent studies have explored cross-subject decoding, we extend recent cross-subject decoding efforts by training a single unified framework jointly across multiple public fMRI datasets and subjects, enabling cross-dataset transfer in addition to cross-subject generalization. We distinguish this multi-dataset unified training setting, where each dataset contributes training data, from a stricter leave-one-dataset-out transfer setting in which the target dataset is excluded from source pretraining and used only for lightweight alignment-layer adaptation. Empirically, our unified model achieves strong semantic reconstruction across datasets (e.g., up to 94.8% CLIP similarity on NSD (AUG) and 0.403 SSIM on BOLD5000 after lightweight finetuning), demonstrating robust cross-subject and cross-dataset transfer.
20 July 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
A machine learning approach to profiling anxiety risk among cancer patients on treatment
PURPOSE: Previous literature has identified multiple risk factors for anxiety among individuals with cancer. However, the relative importance across many interrelated variables is not clear. Further, it is unknown how combined psychosocial factors and demographic/clinical characteristics increase an individual's vulnerability to anxiety. The aims of this study are to systematically test the following: (1) the best predictors of anxiety, (2) the best-performing classifiers for distinguishing high versus low anxiety, and (3) the most informative combinations of psychosocial features and demographic/clinical characteristics that heighten an individual's vulnerability to anxiety. METHODS: Machine learning (ML) models were used. For Aim 1, we tested the predictors of anxiety using the models of regularized regressions, GAMS, and best subset selection. For Aim 2, models tested included support vector machines (SVM), random forest, nearest neighbors, and XGBoost as the classifiers to predict high versus low anxiety. For Aim 3, gradient-boosted decision tree models were trained to examine the most important combinations of candidate predictors. We used SHapley Additive exPlanations (SHAP) to improve the interpretability of our ML models. RESULTS: Across five imputed datasets, using linear regression as the primary benchmark (MSE = 69.945 ± 8.131; MAE = 6.636 ± 0.361), elastic net showed the lowest average prediction error (MSE = 65.486 ± 8.323; MAE = 6.504 ± 0.357), although differences among predictor-based models were modest. For classifiers, random forest achieved the highest mean accuracy (0.751 ± 0.015), kNN achieved the highest PR-AUC (0.797 ± 0.016), and logistic regression achieved the highest ROC-AUC (0.835 ± 0.005). Participation self-efficacy was the most consistent key predictor of anxiety across models. The most important risk combinations of demographic and psychosocial/symptom variables included age-symptom communication barriers, age-cancer coping self-efficacy, ECOG performance status-depressive symptoms, the type of helper-psychological well-being, and education-psychological well-being. CONCLUSION: Our findings may inform risk stratification in clinical settings and facilitate personalized intervention designs aiming at relieving anxiety among cancer patients.
19 July 2026
Read appraisal →Medicine
Effect of physical exercise on executive function in individuals with substance use disorder: A systematic review and meta-analysis
BACKGROUND: The increasing number of individuals with substance use disorder (SUD) has a serious impact on individuals, families, and society. As a complementary therapy, physical exercise has been used in the rehabilitation of individuals with SUD. This study systematically aimed to evaluate the effect of physical exercise interventions on enhancing executive function in individuals with SUD. METHODS: Electronic databases were searched, covering the period from their inception to December 26, 2024. The Cochrane Risk of Bias Assessment Tool was utilized to assess the methodological quality of the included literature. Data were extracted from study graphs using GetData Graph Digitizer 2.26, and Stata 17.0 was also employed to conduct analysis. Grading of recommendations assessment, development and evaluation pro was utilized to evaluate the quality of evidence of outcome indicators. RESULTS: Nine studies, encompassing 752 participants, were included in the analysis. Compared with the control group, physical exercise improved inhibition (Hedges's g = 0.47, 95% CI: 0.29 to 0.65, P < .001) and working memory (Hedges's g = 0.42, 95% CI: 0.16 to 0.69, P = .002), but did not significantly affect cognitive flexibility (Hedges's g = 0.51, 95% CI: -0.22 to 1.24, P = .170). Subgroup analysis revealed that physical exercise with single time of 46 to 60 minutes (Hedges's g = 0.39, 95% CI: 0.05 to 0.73, P = .024), 5 times per week (Hedges's g = 0.45, 95% CI: 0.07 to 0.83, P = .020), duration of 8 weeks (Hedges's g = 0.46, 95% CI: 0.14 to 0.77, P = .004) may improve working memory in individuals with SUD (these subgroup findings are exploratory and should be interpreted as hypothesis-generating). CONCLUSION: Evidence from this study suggests physical exercise may enhance inhibition and working memory in individuals with SUD. However, the improvement in cognitive flexibility requires further investigation in future studies.
19 July 2026
Read appraisal →Neurosurgical review
Stereotactic radiosurgery for treatment-refractory obsessive-compulsive disorder: a systematic review of targets, targeting methods, lesion geometry, and clinical outcomes.
BACKGROUND: Stereotactic radiosurgery (SRS) is a noninvasive option for carefully selected patients with treatment-refractory obsessive-compulsive disorder (OCD), but the literature is heterogeneous in targets, targeting methods, lesion geometry, and outcomes. METHODS: PRISMA-guided systematic review of PubMed, Scopus, and Web of Science. Full-text human studies of radiosurgical capsulotomy for treatment-refractory OCD were included. Narrative synthesis was performed across technical and clinical domains. When ≥3 comparable studies reported the same endpoint, exploratory random-effects single-arm meta-analyses were performed. RESULTS: Twenty-one OCD-specific studies were included (1999-2024). The anterior limb of the internal capsule (ALIC), particularly the ventral ALIC, was the dominant target. Technique and dose reporting varied across cohorts, with commonly reported maximum doses spanning approximately 120-180 Gy, often using 4-mm collimation and single- or double-shot paradigms. Earlier reports primarily used anatomical MRI/coordinate-based targeting, whereas newer studies increasingly incorporated lesion mapping, tractography, and network-based analyses. Eleven cohorts were eligible for quantitative pooling. The pooled clinical response rate was 57.1% (95% CI, 49.1%-64.9%; I²=21.7%), and pooled remission was 36.8% (95% CI, 26.9%-47.9%; I²=0%). Severe delayed radiation-related injury was uncommon but present (6.4%, 95% CI, 3.1%-12.8%; I²=0%). Mechanistic studies suggested that lesion topography and pathway engagement may be more important than lesion size alone. CONCLUSIONS: SRS is a meaningful option for treatment-refractory OCD. The ventral ALIC remains the principal target, but outcomes likely depend on precise target identification and lesion placement. Standardized reporting and prospective evaluation of pathway-informed targeting are needed.
19 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 →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Creative thinking in Parkinson's disease: A systematic review and meta-analysis
INTRODUCTION: Creativity in Parkinson's disease (PD) has aroused research interest due to its neurological underpinnings, which involve brain regions crucial for creative and divergent thinking (a core-process of creative thinking), and because of some patients who displayed an increased artistic drive following dopaminergic treatment. From a cognitive point of view, creative thinking underlies several cognitive abilities, such as executive functions and memory. Therefore, a better understanding of whether it is increased (or, at least, preserved) in PD patients can provide useful insights for sustaining cognitive functioning. The aim of the present study was to investigate whether PD patients regularly assuming dopaminergic medications present higher divergent thinking skills than healthy controls. METHODS: A meta-analysis was conducted according to the PRISMA guidelines to provide a statistical synthesis of the studies. Study quality was assessed using the QUADAS -2 tool. RESULTS: Ten studies were included in the meta-analysis, which indicated the absence of significant differences between PD patients and healthy controls in tasks assessing divergent thinking (Cohen's d = -0,095 (95% CI: -0,308, 0,118)). CONCLUSIONS: Such findings support the notion that divergent thinking can be spared by the disease, maybe constituting a possible resource for patients' cognitive functioning, as it involves those cognitive abilities that can compensate impairments in PD. Clinical implications and guidance to further studies and interventions to support PD patients' cognition were discussed.
17 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 →Journal of ethnopharmacology
Efficacy of Wendan Decoction combined with conventional medicine for depression: a systematic review and meta-analysis
ETHNOPHARMACOLOGICAL RELEVANCE: Wendan Decoction is well-known traditional Chinese medicine formula commonly used in China for the treatment of gastrointestinal disorders and mood disorders related to phlegm-dampness and spleen deficiency. In clinical practice, depending on different symptoms, additional herbs may be included, resulting in a modified formula known as modified Wendan Decoction. AIM OF THE STUDY: To evaluate the efficacy of modified Wendan Decoction plus conventional medicine in the treatment of depression. MATERIALS AND METHODS: We independently conducted a meta-analysis of studies on the efficacy of modified Wendan Decoction for depression from inception to February 28, 2025, using electronic databases, including PubMed, Embase, Cochrane, Web of Science, Wanfang, VIP and SinoMed. Data from randomized controlled trials were extracted to evaluate treatment outcomes based on depression scales. RESULTS: A total of 402 articles were identified through database searches, and 13 studies, encompassing 1117 patients, were included. The initial pooled analysis revealed a significant treatment effect of the experimental group, yielding a standardized mean difference (SMD) of -4.84 (95% CI: -7.54 to -2.14), with high heterogeneity (I2 = 99%). Sensitivity analysis, excluding studies with high risk of bias, demonstrated an SMD of -4.18 (95% CI: -5.26 to -3.10) with moderate heterogeneity (I2 = 83%). Notable regional differences were found, with the study in Southern region exhibiting greater efficacy (SMD = -4.81, 95% CI: -6.38 to -3.23) compared to the study in Northern region (SMD = -3.03, 95% CI: -3.73 to -2.32). CONCLUSIONS: Our meta-analysis supports the efficacy of combining modified Wendan Decoction with conventional medicine for treating depression. Exploratory subgroup results suggest slightly greater improvements in Southern China, though this finding requires cautious interpretation and confirmation in future high-quality trials. PROSPERO Registration ID: CRD420251015690.
15 July 2026
Read appraisal →Journal of affective disorders
Transcranial focused ultrasound for emotion regulation: A systematic review and quantitative summary of human studies.
BACKGROUND: Emotion regulation is a core transdiagnostic process in mood, anxiety, and stress-related disorders. While existing non-invasive brain stimulation approaches, such as transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS), can modulate affective networks, their clinical use is limited by restricted spatial precision and depth penetration. Transcranial focused ultrasound stimulation (tFUS) offers submillimeter focality and access to both cortical and deep subcortical structures, making it a promising tool for affective neuromodulation. METHODS: We systematically searched PubMed, Embase, and PsycINFO for human studies using tFUS to modulate emotion regulation, affective processing, or related symptoms. Eligible studies included randomized controlled trials (RCTs), open-label, and within-subject designs. Data on stimulation parameters, target regions, outcomes, and safety were extracted. Effect sizes were calculated and pooled using a random-effects model, with subgroup analyses by clinical domain. RESULTS: Eleven studies met inclusion criteria, targeting the amygdala (n = 5), prefrontal cortex (n = 5), or subcallosal cingulate cortex (n = 1), with protocols varying in frequency (250-650 kHz), duty cycle (0.5-70%), and number of sessions (1-25). Across six studies reporting behavioral symptom outcomes, the pooled effect was moderate-to-large (Hedges' g = 0.88, 95% CI [0.47, 1.29]), with larger effects in depression-related measures (g = 1.31) than in anxiety-related measures (g = 0.67). Imaging outcomes were reported in a smaller subset of studies and were not included in the pooled estimates. No serious adverse events were reported. CONCLUSIONS: tFUS is a safe and well-tolerated intervention capable of engaging deep affective circuits; however, quantitative estimates remain preliminary given the small and heterogeneous evidence base. Future large-scale, harmonized, and mechanistically informed trials are warranted to refine protocols, establish durability, and optimize translation into clinical practice.
15 July 2026
Read appraisal →NeuroImage
Editorial for the Special Issue on Harmonization Techniques for MRI
This editorial introduces the special issue on neuroimaging harmonization and situates its contributions within the broader methodological landscape of multi-site MRI analysis. As large-scale neuroimaging studies continue to aggregate data across scanners, protocols, and institutions, harmonization has become essential for reducing non-biological variability while preserving meaningful biological signals. We review the major classes of harmonization approaches, including statistical methods based on the ComBat family and deep learning methods that operate at the voxel level. We also review domain generalization strategies designed for previously unseen sites, and network-aware harmonization techniques that go beyond the voxel for connectivity and connectome data. Across these developments, several cross-cutting challenges emerge, including modality-specific performance, preservation of biological information, validation using traveling-subjects and large observational datasets, and the need for scalable, standardized, and privacy-preserving frameworks. Collectively, the articles in this special issue illustrate the rapid progress of the field and highlight that robust harmonization will be critical for enabling reproducible and generalizable discoveries in multi-site neuroimaging.
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 →JMIR mental health
AI Agents Are Coming: 5-Stage Taxonomy of Language-Based AI Systems for Psychiatry, Psychotherapy, and Counseling
The rapid evolution of large language models has accelerated the development of agentic artificial intelligence (AI) systems capable of pursuing autonomous goals, creating an urgent need for structural frameworks in psychiatry and psychotherapy. While existing classifications often draw parallels to autonomous driving, this paper argues that the mental health domain requires a distinct, domain-specific theoretical foundation, as the 2 domains differ fundamentally in their semantic, ideographic, and epistemological demands. Furthermore, they differ in their end goals, for which we introduce terms such as agentic guidance capability. To guide clinicians and researchers through these developments, we propose a 5-stage taxonomy for language-based AI systems that differentiates technical functionality from clinical effectiveness. The taxonomy progresses from level 1 (knowledge level), in which systems perform static benchmark tasks, to level 2 (elementary level), characterized by dynamic engagement in specific therapeutic microskills. At level 3 (integration level), systems achieve consistency across and within modules, as well as basic case-level conceptualization suitable for blended therapy under human oversight. Level 4 (saturation level) describes therapist-in-the-loop systems capable of autonomous functioning with minimal supervision, whereas level 5 (mastery level) represents AI systems that are technically capable of performing autonomous therapy. By distinguishing technical functionality from clinical effectiveness, we conclude that level 4 or level 5 performance does not automatically translate into full treatment effectiveness, even if high treatment fidelity can be achieved. We conclude by emphasizing the need to shift benchmarking from static knowledge tests to dynamic evaluations of therapeutic capabilities in order to safely navigate the transition toward autonomous care.
15 July 2026
Read appraisal →Scientific reports
Satisfaction with telepsychiatry and mental health stigma among health sciences students in Egyptian universities
Health sciences students have a higher incidence of mental health disorders; however, stigma and barriers have complicated help-seeking. Telepsychiatry may be a solution, but acceptance, satisfaction, and stigma in this population, especially in Egypt, remain unclear. This study aimed to evaluate the levels of satisfaction regarding telepsychiatry, stigma perceived with respect to mental illness, among health sciences students towards mental health services. It also aims to assess sociodemographic factors associated with these outcomes. This was a questionnaire-based cross-sectional study conducted among 799 students from various universities in Egypt. It was conducted between August and November 2025, and the questionnaire involved sociodemographic data, the telepsychiatry satisfaction scale (21 items), and the STIG-9 stigma scale. Data were analyzed using descriptive statistics, chi-square and t-tests, and linear regression, with significance set at p < 0.05. Overall, participants had a mean age of 21.4 years, while 66.8% of participants were female. In total, 24.0% agreed, and 18.4% strongly agreed to being satisfied with telepsychiatry, with a mean total satisfaction score of 67.84/105. The mean STIG‑9 stigma score was 14.44/27. Linear regression identified sociodemographic variables independently associated with satisfaction and stigma; higher stigma was also positively associated with higher satisfaction. In this cohort of Egyptian health sciences students with prior telepsychiatry experience, satisfaction with telepsychiatry was positively associated with perceived stigma. These findings suggest that addressing socio-cultural stigma may support the acceptability of telepsychiatry and improve engagement with digital mental health services in similar student populations.
13 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 →Depression and anxiety
The Impact of Psoriasis Treatment on Depression and Suicide Risk: A Systematic Review
BACKGROUND: Psoriasis is a long-lasting inflammatory condition of the skin associated with various comorbidities, including depression and suicidal ideation. Management strategies for psoriasis include symptom alleviation, quality-of-life enhancement, and prevention of disease progression. Psoriasis treatments include topical therapies, phototherapy, oral systemic medications, and biologics. OBJECTIVES: In this review, we evaluate the impact of psoriasis treatments on depression and suicidal ideation in affected patients. METHODS: We systematically searched multiple databases, including PubMed, Scopus, and Web of Science, until September 30, 2024, to identify relevant articles. Studies that examined the effects of psoriasis therapies on depression and suicidal thoughts were included. Data on treatment modalities, psychological health outcomes, and psoriasis severity was obtained. Quality evaluation instruments, such as JBI, consolidated standards of reporting trials (CONSORTs), Center for Evidence-Based Management (CEMBa), and appraisal tool for cross-sectional studies (AXIS), were used to appraise study quality. RESULTS: Ten studies met inclusion criteria, predominantly focusing on biologic therapies. Biologics like guselkumab, brodalumab, and bimekizumab have shown notable reductions in depression symptoms, probably through the alleviation of psoriasis severity and the enhancement of quality-of-life. Suicidal ideation occurred in some cases, especially with brodalumab; however, a definitive causal relationship was not established. CONCLUSIONS: Treatments for psoriasis, especially biologics, have shown some advantages in reducing depression symptoms as well as relieving skin symptoms. However, cautious monitoring is required due to the possible hazards of suicidal thoughts with certain therapeutic options such as biologics. Addressing the complex issues of psoriasis requires comprehensive therapy that includes dermatologists and mental health specialists.
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 →JMIR mental health
Digital Mental Health Research Priorities, Revisited for the AI and Large Language Model Era
Digital mental health has become an established part of mental health care, but the rapid arrival of large language models and other artificial intelligence (AI) tools has refocused attention on the evidence needed to guide the field. This editorial updates the research priorities articulated by JMIR Mental Health in 2023, while reaffirming their emphasis on equity, replicability, privacy, efficacy, and engagement. While the importance of these priorities has not changed in recent years, the urgency with which they must now be applied has. As digital tools become more clinically consequential, research must move beyond demonstrating that a technology is feasible, usable, or novel. The field now needs studies that clarify how these tools work, for whom they are beneficial, under what conditions they may cause harm, and how they can be ethically integrated into care. We call for research that is transparent about the technologies being studied, grounded in meaningful clinical questions, attentive to safety, and designed to produce knowledge that remains useful as specific products and models change.
13 July 2026
Read appraisal →Philosophical transactions of the Royal Society of London. Series B, Biological sciences
The hippocampus as a generative model
A generative model can be defined as a model of the latent causes of sensory input that can be used to generate new data samples. By examining empirical evidence and computational theory, we propose that the hippocampus can be characterized as a generative model. The hippocampus is a brain region important for memory. Recordings of neural activity from the hippocampus have led to the view that the hippocampus represents a cognitive map by abstracting a low-dimensional representation of the external world. We extend this view to suggest the hippocampus represents the latent, unobserved causes of sensory data by virtue of the position of the hippocampus within the deep cortical hierarchy. These representations of unobserved latent causes endow the hippocampus with capacity to generate new data samples that allow exploration of future hypotheticals and provide an internally generated training signal back to the generative model. We explore how perturbations to the hippocampal generative model may explain core symptoms of neuropsychiatric disorders such as those observed in psychosis. Together, this perspective provides a unified account of hippocampal function that explains how computations performed by the hippocampus support higher-order cognition and adaptive behaviour. This article is part of the theme issue 'The role of hippocampal predictions in cognition: bridging perception and memory'.
11 July 2026
Read appraisal →Cell host & microbe
Adjunctive fecal microbiota transplantation for major depressive disorder: A randomized, double-blind, placebo-controlled trial
Gut microbiota may influence antidepressant treatment outcomes, yet whether targeted modulation can enhance efficacy remains unclear. We conducted a randomized, double-blind, placebo-controlled trial in patients with major depressive disorder, administering a 2-week course of fecal microbiota transplantation (FMT) capsules or placebo as an adjunct to escitalopram (ChiCTR2300071421). Remission rates at week 8 did not differ significantly between groups, but FMT produced greater reductions in Hamilton Depression Rating Scale, 17-item version (HAMD-17) scores at weeks 2 and 8. FMT was well-tolerated with a safety profile comparable to placebo. Multi-omics analyses show durable donor microbial engraftment and enrichment of beneficial Lachnospiraceae and Oscillospiraceae taxa. Microbial remodeling is accompanied by an increase in serum bile acids that correlate with the alleviation of depressive symptoms. Mediation analysis supports a bile-acid-mediated suppression of inflammatory pathways linking microbial changes to antidepressant effects. Overall, FMT may provide a safe avenue to enhance escitalopram efficacy through microbiota-directed regulation of bile-acid metabolism and inflammation.
10 July 2026
Read appraisal →Psychological medicine
Extreme heat and mental health: systematic review and qualitative investigation of risk and protective factors
BACKGROUND: Extreme hot weather poses increasing risks to mental health. Yet, factors affecting vulnerability are under-researched. This mixed-method study integrates a systematic review and qualitative investigation to identify risk and protective factors for heat-related mental health issues, leading to the co-development of a screening tool. This could inform future research and, pending validation in clinical settings, support mental health professionals in assessing vulnerability among service users. METHODS: We searched PubMed and Web of Science for publications on extreme heat, mental health, and risk/protective factors. In addition, we conducted six focus groups with 21 people with lived experience of heat and/or mental illness and 12 healthcare professionals. Transcripts were analyzed using thematic content analysis and informed the co-development of the screening tool. RESULTS: Out of 764 articles identified by the systematic review, 47 were included. Evidence emerged for age, sex, existing mental illness, ethnicity, and socioeconomic status as risk factors. However, findings were inconsistent between studies, likely due to differences in study population and methodology. Protective effects included good physical health, social support, and exposure to green spaces. Our qualitative investigation identified additional risk and protective factors related to: (1) behavioral adaptability, (2) personal heat sensitivity, and (3) disparities in heat exposure. The resulting screening tool, HEAT-MH (Heat Exposure Assessment Tool for Mental Health), contains 15 questions on previous experiences of heat, general health, and lifestyle. CONCLUSIONS: The mental health impacts of extreme heat depend on a range of risk and protective factors, including demographic, socioeconomic, health, and lifestyle characteristics.
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 →The Journal of international medical research
Examining the state of telehealth for mental health and substance use care after the coronavirus disease 2019 pandemic: An integrative review.
ObjectiveThe objective of this integrative review was to synthesize literature and provide implications for clinical practice on telehealth use among patients with serious mental illness and substance use disorders following the coronavirus disease 2019 pandemic.MethodsAn integrative review guided by Socio-Technical Systems Theory was applied. The PubMed, Cumulative Index to Nursing and Allied Health Literature, PsycINFO, Medline, Academic Search Complete, and Gale Health and Wellness databases were searched for publications from 1 January 2019 to 1 December 2024. Articles selected according to the established inclusion and exclusion criteria were evaluated using a rapid critical appraisal checklist developed by Fineout-Overholt and Melnyk.ResultsAmong the 172 articles reviewed, 16 peer-reviewed and 2 government publications were included. Four themes were identified: (a) treatment adherence; (b) satisfaction reported by patients and providers; (c) telehealth policy developments; and (d) access to services. Telehealth supported continuity of care, improved satisfaction, and improved access. Technological and financial barriers restricted equitable access. Policy changes enabled broader adoption; however, regulatory approaches varied across jurisdictions.ConclusionTelehealth remains an integral method for delivering mental health and substance use care following the coronavirus disease 2019 pandemic. Greater focus is needed on long-term effectiveness and limitations of telehealth.
3 July 2026
Read appraisal →Psychiatric services (Washington, D.C.)
Psychiatric Risk Associated With Large Language Model Chatbots: An Emerging Concern
Large language model chatbots such as ChatGPT have been adopted by millions of users, yet their psychiatric implications are poorly understood. Recent media reports describe 26 apparent instances of psychosis and two instances of depression and suicide in the context of heavy chatbot use, often marked by delusions seemingly reinforced by the model. Because chatbots simulate conversation but are statistical systems optimized for agreement, they may amplify distorted beliefs. Research is needed to determine whether these reports represent a genuine pattern of risk and to identify vulnerable populations. Clinicians should screen for chatbot use and discuss these systems with patients.
2 July 2026
Read appraisal →The Behavioral and brain sciences
Large language models have learned to use language.
Acknowledging that large language models have learned to use language can open doors to breakthrough language science. Achieving these breakthroughs may require abandoning some long-held ideas about how language knowledge is evaluated and reckoning with the difficult fact that we have entered a post-Turing test era.
2 July 2026
Read appraisal →Journal of the American Medical Informatics Association : JAMIA
TRUST: an large language model-based dialogue system for trauma understanding and structured assessments
OBJECTIVES: While large language models (LLMs) have been widely used to assist clinicians and support patients, no existing work has explored dialogue systems for standard diagnostic interviews and assessments. This study aims to bridge the gap in mental healthcare accessibility by developing an LLM-powered dialogue system that replicates clinician behavior. MATERIALS AND METHODS: We introduce TRUST, a framework of cooperative LLM modules capable of conducting formal diagnostic interviews and assessments for post-traumatic stress disorder (PTSD) following the Clinician-Administered PTSD Scale for DSM-5 (CAPS-5). To guide the generation of appropriate clinical responses, we propose a Dialogue Acts schema specifically designed for clinical interviews. Additionally, we develop a patient simulation approach based on real-life interview transcripts to replace time-consuming and costly manual testing by clinicians. RESULTS: A comprehensive set of evaluation metrics is designed to assess the dialogue system from both the agent and patient simulation perspectives. Expert evaluations by conversation and clinical specialists show that TRUST performs comparably to real-life clinical interviews. DISCUSSION: Our system performs with clinical quality approaching that of human clinicians, with room for future enhancements in communication styles and response appropriateness. CONCLUSIONS: Our TRUST framework shows its potential to facilitate mental healthcare availability.
2 July 2026
Read appraisal →Journal of Alzheimer's disease : JAD
Early diagnosis of Alzheimer's disease through handwriting analysis and deep learning: A review.
Alzheimer's disease (AD) is one of the most prevalent neurodegenerative disorders worldwide, requiring early identification for timely intervention and to slow disease progression. However, existing diagnostic approaches, while effective at later stages, remain limited in detecting early-stage AD. Handwriting analysis has recently emerged as a non-invasive, cost-effective, and ecologically valid digital behavioral biomarker that reflects neurocognitive impairment. This review examines the role of handwriting as a neurocognitive marker for AD, focusing on integrating deep learning methodologies to enhance early diagnostic accuracy. It also elucidates the neurocognitive mechanisms linking handwriting behavior and AD, addressing current methodological and translational challenges. We performed a PRISMA-informed structured literature search and narrative synthesis of handwriting- and drawing-based studies for detecting AD/mild cognitive impairment (MCI), including offline handwriting images and online pen-stroke kinematics captured by digital devices. Task paradigms, data dimensions, preprocessing pipelines, modeling strategies (traditional machine learning and deep learning), evaluation practices, and translational considerations were summarized, and studies were organized by detection purpose and analytic approach. Our findings show that handwriting-based models generally discriminate AD/MCI from healthy controls with accuracy exceeding 80%, while deep learning models (e.g., convolutional neural network and multimodal Transformer fusion) approach 90% in structured tasks like clock drawing and figure copying. Online kinematic markers (e.g., reduced velocity, prolonged in-air time, increased pausing, and pressure instability) recur across studies, and multimodal integration with speech, gait, or facial signals can further improve sensitivity and ecological validity, although most studies are small and single-center.
2 July 2026
Read appraisal →Current opinion in psychiatry
Opportunities and risks of large language models in digital interventions for substance use disorders
PURPOSE OF REVIEW: Large language models (LLMs) are increasingly integrated into digital mental health tools, yet their role in substance use disorder (SUD) interventions remains poorly understood. This review synthesizes emerging evidence on the opportunities and risks of applying LLMs across the digital SUD care continuum. RECENT FINDINGS: Studies report promising applications in early detection, personalized support, continuous monitoring, and relapse prevention. LLMs demonstrate capacity to extract substance-use signals from natural language, generate supportive and motivational responses, and interpret narrative data for patient-reported outcomes. However, risks are substantial. LLMs can produce inaccurate or hallucinated content, may reinforce stigma or demographic bias, and can generate misleading or potentially unsafe advice. Privacy concerns are amplified in SUD contexts, where sensitive data are often managed outside regulated healthcare systems. Existing regulatory frameworks such as the EU AI Act or U.S. device regulations, do not yet provide clear governance for anonymous, AI-supported SUD interventions. SUMMARY: LLMs have potential to expand scalable, low-threshold support for SUDs, but their safe deployment requires validation, bias mitigation, transparent data governance, and robust human oversight. Evidence remains preliminary, and clinical integration should proceed cautiously.
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 →Diving and hyperbaric medicine
Bipolar spectrum disorders in divers: risks, recognition, and recommendations.
Bipolar disorder is a recurrent psychiatric condition characterised by episodic mood disturbances, residual functional impairment, and high rates of psychiatric and medical comorbidity. While many individuals achieve symptomatic remission, persistent deficits in cognition, emotional regulation, and insight may remain, raising concerns for participation in safety-critical activities such as scuba diving. This systematic review synthesised evidence from psychiatric, occupational, aviation, and diving medicine literature to examine the clinical course of bipolar disorder, treatment considerations, functional outcomes, and safety-relevant factors pertinent to fitness-to-dive assessments. Bipolar disorder exhibits marked heterogeneity in syndromal and functional outcomes. Even during euthymia, subtle impairments in attention, executive functioning, and decision-making may persist. Pharmacological stability is essential for diving, but treatment regimens, particularly lithium use, polypharmacy, and antidepressant therapy, introduce additional considerations. Comorbidity, circadian disruption, sleep deprivation, and reduced insight during early relapse further complicate risk assessment. Empirical data on diving outcomes in individuals with bipolar disorder are scarce, necessitating reliance on expert opinion and extrapolation from related safety-critical domains. Fitness-to-dive assessments in bipolar disorder should prioritise sustained functional stability, reliable treatment adherence, and illness insight over symptom absence alone. A cautious, individualised approach is warranted, incorporating medication effects, comorbidity, operational context, and relapse-prevention planning, supported by collaboration between mental health professionals and diving medical examiners.
30 June 2026
Read appraisal →PloS one
Hybrid deep learning for mental workload classification using EEG with enhanced preprocessing and interpretability
Mental workload classification is critical in safety-sensitive fields such as healthcare and aviation. However, electroencephalography-based approaches still face challenges with generalizability, noise robustness, and interpretability. In this study, we propose an integrated hybrid deep learning framework to address these limitations and enable robust, interpretable electroencephalography-based mental workload classification. The proposed approach uses a Variational Autoencoder to enhance noise reduction and feature extraction from band-wise topographical videos, a Convolutional Block Attention Module to adaptively focus on important spatial-channel Electroencephalogram features, and a Bidirectional Long Short-Term Memory network to capture complex temporal dependencies under leave-one-subject-out cross-validation. We conducted ablation studies to identify each architecture component's contribution and sensitivity analyses to determine the optimal parameters. The model achieved the highest overall accuracy among the baselines and reached an average accuracy of 83.9% across subjects for classifying four mental workload levels. Ablation studies confirmed the added value of all three architecture components for improving performance. Sensitivity analyses identified optimal parameters, including a 10-second window length that balances temporal context and specificity. For better interpretability and neurophysiological insight, we used Gradient-weighted Class Activation Mapping to visualize key frontal-parietal brain regions and frequency bands associated with workload dynamics. Future research could explore adaptive windowing strategies, multimodal data integration, cross-dataset benchmarking, and evaluation against transformer-based and graph neural network architectures under consistent subject-independent evaluation settings to further enhance model generalizability.
28 June 2026
Read appraisal →Science advances
AI-driven tripartite classification for optimizing wearable bioelectronics in depression management
Current disease-sensing devices primarily focus on distinguishing between healthy and diseased states, effective for diagnosis but limited in guiding optimal intervention timing for prevention. We developed a tripartite framework identifying pre-disease state in depression, a reversible phase preceding irreversible onset. Using complex systems theory, we analyzed early-warning signals emerging as biological systems approach critical transitions. Continuous monitoring of nine multimodal biomarkers-spanning electrophysiological, behavioral, and biological-enabled classification into normal, pre-disease, and disease states by quantitatively defining critical points. An artificial intelligence agent classified disease states with 95.2% accuracy using multimodal data, enabled by ultrasoft neural probes for stable, low-damage recordings. Therapeutic validation with a skin-attachable wireless vagus nerve stimulator integrating soft three-dimensional electrodes demonstrated superior efficacy during pre-disease states. Subjects treated during pre-disease showed faster recovery and greater therapeutic responses, while those treated after disease onset failed to achieve full recovery. This framework provides evidence-based rationale for early intervention.
27 June 2026
Read appraisal →BMJ open
Effectiveness of peer support interventions to improve mental health outcomes after miscarriage: a systematic review and call for high-quality evidence.
OBJECTIVES: Peer support is being integrated into the new maternal mental health services in England to further the development of the recovery approach in relation to loss. Psychological support after miscarriage (pregnancy loss prior to viability) is often overlooked, despite significant psychological morbidity. This systematic review explored the effectiveness of peer support interventions to improve mental health outcomes after miscarriage. DESIGN: Systematic review DATA SOURCES: A comprehensive systematic search across nine databases (MEDLINE, CINAHL, APA PsycINFO, Web of Science (all databases), EMBASE, CENTRAL, LENS.org, British Nursing Index and Health Management Information Consortium) was conducted in June 2025. Grey literature was identified through website searching, contact with topic experts and a national call for evidence. ELIGIBILITY CRITERIA: Study designs with a quantitative evaluative component or mixed-methods studies reporting effectiveness were eligible if they involved women and/or partners who had experienced miscarriage and been offered a peer support intervention. Any peer support versus control (no treatment, wait list and usual care) or peer support versus another psychosocial intervention was eligible for inclusion. Studies that report any of the following broad groups of outcomes (whether validated measures or by self-report) were eligible for inclusion: (a) personal recovery, (b) mental health recovery, (c) health service use and (d) social outcomes. DATA EXTRACTION AND SYNTHESIS: Two independent reviewers used standardised methods to search and screen for eligible studies. RESULTS: Of the 4342 titles screened, 100 potentially relevant full-text papers were retrieved and screened resulting in seven randomised controlled trials and two controlled trials identified across 10 papers. Of these, seven did not evaluate a peer-led intervention, one reported only on women who had experienced pregnancy loss later than 24 weeks gestation, and one reported a peer support intervention for those who experienced pregnancy loss at any age of gestation but did not disaggregate data for those who experienced miscarriage. Thus, no studies were eligible for inclusion. This indicates a significant gap in the current literature. The inconsistencies and limitations in existing research approaches are explored in detail. CONCLUSIONS: This systematic review has identified an evidence gap as there is currently no robust evidence for the effectiveness of peer support interventions after miscarriage. Given the drive for the inclusion of peer support in new maternal mental health services, there is therefore a need for targeted intervention research to provide reliable evidence to determine effective peer support interventions for this population. PROSPERO REGISTRATION NUMBER: CRD42024518248.
25 June 2026
Read appraisal →Journal of medical Internet research
Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review
BACKGROUND: Driven by recent advances in artificial intelligence (AI), particularly in medicine, audio-based voice and speech biomarkers are increasingly investigated for various medical applications as a complementary or even alternative modality to traditional medical devices. The adoption of deep learning techniques in recent literature is motivated by their superior performance compared to classical machine learning methods. However, ethical and regulatory concerns regarding the black-box nature of these models have limited their integration into clinical workflows. Consequently, explainable artificial intelligence (XAI) has recently been used to address this issue by generating explanations for opaque model outputs. Ideally, medical XAI systems aim to provide human-understandable, clinically grounded explanations essential for enhanced AI trustworthiness and, thereby, facilitate adoption into real-world clinical settings. OBJECTIVE: We conduct a systematic literature review of XAI methods applied for explaining deep learning techniques in audio-based voice and speech clinical applications. We aim to identify what XAI methods have been used to explain the decisions of deep learning voice and speech AI systems in health care, as well as XAI-informed insights. Additionally, we aim to contextualize these findings with respect to clinical applicability and stakeholder relevance. Lastly, we identify opportunities and recommendations for future clinical audio XAI design. METHODS: We used PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Six electronic databases (IEEE Xplore, ACM Digital Library, Scopus, PubMed, Web of Science, and Nature) were searched for papers published between January 2015 and February 2025. Eligible studies applied explainability or interpretability methods to deep learning models for voice or speech audio in health care contexts. Risk of bias was assessed using PROBAST+AI (Prediction Model Risk of Bias Assessment Tool). The results were thematically synthesized across explainability categories, input representations, clinical domains, validation strategies, and stakeholder considerations. RESULTS: A total of 30 studies met the inclusion criteria. These studies used a range of explainability approaches, including gradient-based methods, perturbation-based techniques, surrogate model-based methods, model-internal representation analyses, concept-based detectors, and attention-based explanations. Applications spanned diverse clinical domains, including voice disorders, neurodegenerative diseases, psychiatric conditions, and traumatic brain injury. Overall, results indicate that most studies relied primarily on qualitative interpretation of explainability outputs, with limited quantitative validation of explanation consistency across external datasets. Furthermore, none of the included studies explicitly conducted human-in-the-loop evaluations with relevant stakeholders, highlighting a substantial gap in stakeholder alignment. CONCLUSIONS: Current XAI practices in clinical voice and speech analysis are limited by insufficient validation, lack of domain-specific design, and misalignment with clinical stakeholder needs. This review highlights opportunities for developing validated, audio-aware, and stakeholder-centered XAI approaches to support trustworthy clinical deployment. Interpretation of these findings should consider limitations related to single-reviewer study selection, potential high-risk of bias, and the repeated use of benchmark datasets.
25 June 2026
Read appraisal →BMJ global health
Lifestyle interventions for dementia prevention in low- and middle-income countries: a systematic review.
INTRODUCTION: By 2050, two-thirds of people with dementia will live in low-and-middle-income countries (LMICs). However, multimodal prevention lifestyle interventions for people at risk are being developed predominantly in higher-income countries. METHODS: We systematically reviewed randomised controlled trials (RCTs) evaluating non-pharmacological interventions in individuals with mild cognitive impairment and subjective cognitive decline in LMICs. We assessed quality using the Mixed Methods Assessment Tool, meta-analysed and synthesised evidence. RESULTS: We included 25 RCTs from six countries (most in China, n=17), involving 1304 participants. In the 15 studies with sufficient data to meta-analyse, we found significant positive effects on cognition favouring interventions (1.49 (standardised mean difference, 95% CI 1.06 to 1.93)). There was significant publication bias. We classified interventions into exercise, multidomain and arts/creative expression. Exercise (1.67, 95% CI 1.24 to 2.11, n=8) and multidomain (1.22, 95% CI 0.22 to 2.21, n=5) had replicated evidence of effectiveness. There was insufficient data to meta-analyse the arts/creative category. CONCLUSION: We propose greater consideration and investment in the development of interventions accounting for specific LMIC contexts from the outset, so they are acceptable and used by local services. PROSPERO REGISTRATION NUMBER: CRD42023403908.
24 June 2026
Read appraisal →Journal of neural engineering
Uncertainty-aware multi-path framework with dynamic arbitration for Alzheimer's disease MRI classification
Objective.As a progressive neurodegenerative disorder, Alzheimer's disease (AD) requires early and accurate diagnosis to delay pathological progression and improve clinical outcomes. Recently, Deep-learning methods based on structural MRI (sMRI) have been widely used for computer-aided AD diagnosis. However, reliable deployment remains challenging due to its sample-specific heterogeneity and the lack of explicit mechanisms for handling prediction uncertainty. In addition, common test-time perturbations and augmentation-based inference strategies could introduce anatomical inconsistency in medical images, leading to unstable decisions for hard cases.Approach.Accordingly, TriPathNet with Arbiter, as an uncertainty-aware multi-path framework, is proposed in the study. The framework learns spatially complementary representations via multi-view parallel encoding with metric-guided coordination. In addition, an augmentation-aware arbitration mechanism is introduced to adaptively correct predictions for high-uncertainty samples during inference.Main results.On the ADNI dataset for the AD vs. cognitively normal task, the proposed method achieves 98.85% accuracy, 99.18% sensitivity, 97.06% specificity, and 99.61% AUC, yielding satisfactory performance.Significance.The results indicate that TriPathNet with Arbiter provides an accurate and robust solution for sMRI-based computer-aided AD diagnosis, maintaining stable performance under test-time perturbations.
20 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 →European journal of pharmacology
Inhibition of the serotonin transporter and risk of heart valve disease: A systematic review and meta-analysis
BACKGROUND: The serotonin transporter (SERT) plays an essential role in regulating a wide range of physiological and psychological processes, including neurotransmitter homeostasis, circadian rhythm, sleep regulation, platelet function, immune modulation, mood and emotions. Although SERT was initially studied in the context of neurological and psychiatric disorders, growing evidence highlights how its off-target exogenous inhibition, and the resulting dysregulation of serotonin signaling, can promote valvular interstitial cell proliferation, extracellular matrix remodeling, and fibrosis, thereby contributing to the pathophysiology of heart valve disease (HVD). Despite the increasing interest in the role of SERT in valve biology, mechanistic studies remain limited, and a deeper understanding of the implications of commonly used SERT-targeting drugs in the progression of HVD is still needed. METHODS: In this review, we examine the impact of drug-induced SERT inhibition on HVD, integrating findings from cellular studies, animal models, and clinical data to better understand the risks associated with chronic use of SERT inhibitors. To strengthen the clinical relevance of our review, we also performed a meta-analysis of published clinical studies on SERT-modulating drugs, which revealed a significant association between the use of these medications and increased HVD risk (OR = 2.76). CONCLUSIONS: Our analysis supports the implementation of clinical screening for high-risk patients, such as those with diabetes, hypertension, or pre-existing valvular abnormalities, prior to serotonergic drug recommendations. Ultimately, a deeper understanding of the role of serotonin and SERT in valvular pathophysiology may guide safer prescribing practices and facilitate the development of novel therapeutics for managing or preventing the progression of HVD.
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 affective disorders
Heart rate variability as a predictor of future depressive symptoms: A systematic review and meta-analysis
Depression has limited early detection strategies. Heart rate variability (HRV), an indicator of autonomic regulation, has been linked to depression cross-sectionally, but its prospective value remains uncertain. We performed a meta-analysis and systematic review examining whether HRV is prospectively associated with later depressive symptom levels. PubMed, Embase, Cochrane, and PsycINFO (inception November 3, 2024) were searched for longitudinal studies of initially non-depressed baseline HRV and at least one-month follow-up assessing depressive or internalizing symptoms. Meta-analyses used multilevel random-effects models; prediction models were synthesized by HRV's statistical role. From 12,023 records, 56 studies entered the meta-analysis, and 62 the qualitative synthesis. HRV showed small but significant negative correlations with later depressive symptoms for frequency-domain indices (HF-HRV (r = -0.16, 95% CI -0.22 to -0.11), LF-HRV (r = -0.19, 95% CI -0.27 to -0.10), and VLF-HRV (r = -0.19, 95% CI -0.28 to -0.11)), and time-domain indices (RMSSD (r = -0.17, 95% CI -0.28 to -0.05) and SDNN (r = -0.15, 95% CI -0.26 to -0.03)). Across prediction-models, HRV showed direct effects in 43% and conditional effects in 57%, involving RSA and nonlinear patterns. HRV reflects psychophysiological mechanisms of depression vulnerability, but its prospective effects are small and context-dependent. Clinically, HRV may function as an adjunctive tool for identifying individuals who are more vulnerable to adversity and for informing preventive strategies, including the personalization of biofeedback and other regulation-focused interventions.
16 June 2026
Read appraisal →Biological psychiatry
Systematic Review and Mega-Analysis of the Peripheral Blood Transcriptome in Depression Implicates Dysregulation of Lymphoid Cells and Histones
BACKGROUND: Depression has been associated with transcriptomic changes in peripheral blood. However, the contribution of specific immune cell subsets or pathways remains unclear, and findings have been variable across previous studies, which have not tended to account for sample cellular composition. METHODS: We performed a systematic review of peripheral blood transcriptome studies in depression. For the 5 datasets meeting criteria (total N = 6011), we performed harmonized reprocessing and cell composition-adjusted differential gene and transcript analyses, followed by a bias- and inflation-adjusted weighted z score mega-analysis. We investigated the biological pathways and cell subsets implicated by the results. We also performed a sex-stratified gene network mega-analysis using consensus weighted gene coexpression network analysis (WGCNA). RESULTS: Few genes showed robust differential gene expression (DGE) in depression. Depression was reproducibly associated with decreases in replication-dependent histones and with a decrease in oxidative phosphorylation pathways in females only. Cell source analyses implicated lymphoid cells (T cells and natural killer cells) as likely contributors to the depression differential expression signature. WGCNA mega-analysis revealed multiple consensus modules associated with depression, with a PUF60-related module upregulated in both female and male depression in sex-stratified analyses. Two genes predicted to be causally relevant to depression by transcriptome-wide association studies (GPX4 and GYPE) showed significant DGE. CONCLUSIONS: These results are convergent with immunogenetic evidence implicating lymphoid cell dysregulation in depression, while also highlighting histone alterations as a key molecular signature in depression. They also indicate the importance of large-scale datasets for biomarker discovery in the context of heterogeneous disorders such as depression.
16 June 2026
Read appraisal →Journal of affective disorders
MDD-thinker: A reasoning-enhanced large language model for diagnosis of major depressive disorder.
BACKGROUND: Major depressive disorder (MDD) is a leading cause of global disability and poses a substantial public health burden. However, current diagnostic approaches largely rely on subjective assessments and lack the ability to integrate heterogeneous clinical and sociodemographic information. Recent advances in large language models (LLMs) offer new opportunities to support MDD diagnosis through reasoning over complex data, yet their clinical applicability is constrained by challenges related to interpretability, hallucinations, and reliance on synthetic data. METHODS: We propose MDD-Thinker, an LLM-based diagnostic system that integrates supervised fine-tuning (SFT) and reinforcement learning (RL) to enhance reasoning and interpretability under the evaluated conditions. Using the UK Biobank dataset, we constructed 40,000 structured reasoning samples and incorporated an additional 10,000 records from publicly available mental health datasets. MDD-Thinker was trained on these heterogeneous textual data and evaluated against conventional machine learning models, deep learning methods, and representative LLM baselines in terms of diagnostic performance and interpretability. RESULTS: MDD-Thinker achieved high performance in MDD diagnosis, with an accuracy of 0.8268 and an F1-score of 0.8081, showing better performance than conventional machine learning models, deep learning approaches, and representative LLM baselines on the evaluated dataset. Beyond predictive accuracy, it consistently produced structured reasoning paths that were clinically coherent, enabling transparent interpretation of diagnostic decisions in the evaluated experiments. The integration of SFT and RL contributed to notable improvements in both diagnostic reliability and reasoning quality. CONCLUSION: MDD-Thinker demonstrates the potential of reasoning-enhanced LLMs for large-scale MDD diagnosis under the evaluated settings. By jointly optimizing accuracy, interpretability, and efficiency, the proposed system provides a scalable and explainable approach for intelligent psychiatric assessment within the scope of the study, highlighting the potential of reasoning-oriented LLMs in mental health care.
15 June 2026
Read appraisal →Journal of affective disorders
Brain aging in bipolar disorder using a neuroimaging and machine learning-derived metric: Findings from the ENIGMA BD Working Group.
BACKGROUND: Bipolar disorder (BD) is associated with clinical and biological markers of premature aging. In this largest study of brain age in BD to date, with 2919 participants, we compared brain-predicted age difference (brain-PAD) in individuals with BD and healthy comparison (HC) participants. Brain-PAD is a machine learning-estimated metric that quantifies the difference between an individual's predicted brain age and their chronological age, a potential clinical bio-signature of premature brain aging. Within individuals with BD, we also examined how medication and clinical characteristics were related to brain-PAD. METHODS: Age was predicted from 77 MRI measures of regional subcortical and lateral ventricle volumes, cortical thickness, and surface area for 1342 BD and 1577 HC adult participants, aged 18-75 yrs. old (μ = 37.2; SD = 12.3), from the curated ENIGMA Bipolar Disorder working group (ENIGMA-BD) and leveraging an ENIGMA machine learning model previously trained and validated using independent samples. Chronological age was subtracted from predicted age to produce an individual-level estimate known as brain-PAD. Linear mixed models (adjusting for sex and age as fixed effects and site as a random effect) were used to examine group differences and clinical associations. RESULTS: BD was associated with higher brain-PAD, compared to HC, primarily among older patients, as demonstrated by a significant age by diagnosis interaction (+0.05 [SE: 0.02] years). Individuals with BD on antiepileptic (AED) medications only (+3.20 [SE: 0.78] years) or on both AED and second-generation antipsychotics (SGA) (+3.74 [SE: 0.89] years) demonstrated greater brain-PAD compared to individuals who were not on any of the examined medications. Those taking lithium, whether alone or with AED and SGA independently, showed no difference in brain-PAD compared to individuals not taking any of the examined medications. However, individuals who were taking lithium showed lower brain-PAD compared to those on AED (-4.48 [SE: 0.84] years) or AED and SGA (-5.01 [SE:0.92] years). Individuals with a BD I subtype diagnosis had a higher brain-PAD (+1.50 [SE:0.55] years) compared to those with BDII or subtypes that are not otherwise specified (NOS). CONCLUSIONS: Results from this study suggest compounding effects of BD diagnosis and older age on brain-PAD, an ML-derived summary metric of structural alterations. Within BD, brain-PAD was differentially related to medication use, consistent with prior findings from ENIGMA-BD. Notably, AED use was generally related to more advanced brain age. Lithium use, alone or in combination with other medications, was not associated with advanced brain age, suggesting a possible neuroprotective effect of lithium. Brain-PAD as an ML-derived summary metric of structural alterations of the brain may provide clinical utility in assessing long-term holistic brain health to monitor the effectiveness of lifestyle modifications or treatments over time. LIMITATIONS: The cross-sectional nature of the study design and the limited granularity of the clinical data limit interpretation. Longitudinal studies with detailed chronicity data, medications and clinical measures overtime will improve brain-PAD modeling in BD.
15 June 2026
Read appraisal →Journal of affective disorders
Implementing artificial intelligence (AI) to facilitate health outcomes in mood disorders: Application versus aspiration.
The burden of depressive and bipolar disorders at the individual and societal level are extraordinary and increasing. For decades, evidence-based treatments for these conditions have been established but outcomes amongst individuals with lived experience remains suboptimal. Notwithstanding calls to close gaps between current practice and best practice, there is an absence of evidence that overall health outcomes are significantly improving. Artificial Intelligence (AI) is the cornerstone of the digital revolution. Currently, AI sources (e.g., Open Source) are widely accessed by healthcare providers and persons with lived experience for search queries and decision support. The aspiration for AI-informed medical practice is to improve health outcomes by assisting in timely diagnostic detection, illness monitoring, informing treatment selection, integrating multimodality care, decreasing barriers to access and facilitating scalability to psychosocial interventions. Against this background, rigorous evidence is still needed to empirically demonstrate transformative improvement in each of the aforementioned areas. In addition, multiple ethical, technical, scientific and economic questions are not adequately answered including aspects of confidentiality and patient engagement. This short commentary endeavors to succinctly summarize the evidentiary base as it relates to the capabilities that AI offers currently, in the near and more intermediate term. The overarching aim is to provide readers with an up-to-date understanding of what aspects of AI are currently applicable versus those that are aspirational.
15 June 2026
Read appraisal →JMIR cancer
Identification of Early Signs of Mental Health Disorders in Older Survivors of Cancer Using Patient-Generated Health Data: Observational Study
BACKGROUND: Older survivors of cancer face heightened risk of depression and anxiety related to cancer experiences, fear of recurrence, and aging-related difficulties. Conventional mental health monitoring approaches, such as clinical assessments and even electronic patient-reported outcomes, are limited by recall bias, patient burden, and infrequent data collection. Emerging patient-generated health data from wearables and smart home devices offer passive, low-burden, continuous monitoring, but their ability to capture mental health risks in older survivors of cancer remains unclear. OBJECTIVE: This study aims to explore whether patient-generated health data collected in the wild, either passively or actively, can classify older survivors of cancer as having or not having signs of anxiety and depression based on Patient Health Questionnaire-4 (PHQ-4) scores and to assess the potential added value of passive monitoring modalities, such as smart plugs. METHODS: This study recruited 41 older survivors of cancer (mean age 72.3, SD 6.81 years) from the LifeChamps project. Over a 12-week period, participants were monitored using an activity tracker to measure physical activity, sleep, and physiological metrics; a smart scale to capture weight and body composition; and a smart plug to track television use as a proxy for sedentary television viewing. Mental health status was self-reported via the PHQ-4 questionnaire in a mobile app. Machine learning models were trained to classify mental health risk based on features derived from each sensor modality, both independently and in combination. RESULTS: Tree-based gradient boosting models showed good performance in classifying PHQ-4-defined mental health risk. The best-performing configuration, combining smart plug and smart scale features, achieved a mean F1-score of 0.77 (SD 0.15) and a mean area under the receiver operating characteristic curve (AUC) of 0.85 (SD 0.10) across 3 repeated train-test splits. Standalone smart plug models, based solely on passive television use patterns, achieved a mean F1-score of 0.66 (SD 0.04) and a mean AUC of 0.71 (SD 0.06), outperforming models that relied only on activity tracker data (mean F1 0.59, SD 0.2). Multimodal combinations tended to improve average performance but did not consistently yield large gains over the strongest single-modality configurations, likely reflecting adherence-related data loss for wearables and scales. Crucially, passive monitoring of television use patterns emerged as a promising behavioral proxy measure of mental health states. CONCLUSIONS: This study pioneers the use of passively collected data (eg, smart plugs) for mental health monitoring in older survivors of cancer, demonstrating their potential. Smart plugs capture behavioral patterns without user burden, with reasonable standalone performance (mean AUC 0.71, SD 0.06), positioning them as a promising low-burden modality. Future work should validate findings in larger independent cohorts and in prospective clinical workflows. Such technologies could transform monitoring for vulnerable populations, enabling scalable, inclusive care while reducing health care burdens.
14 June 2026
Read appraisal →Journal of mathematical biology
Spiking neural models for decision-making tasks with learning.
In cognition, response times and choices in decision-making tasks are commonly modeled using Drift Diffusion Models (DDMs), which describe the accumulation of evidence for a decision as a stochastic process, specifically a Brownian motion, with the drift rate reflecting the strength of the evidence. In the same vein, the Poisson counter model describes the accumulation of evidence as discrete events whose counts over time are modeled as Poisson processes. This model has a spiking neurons interpretation as these processes are used to model neuronal activities. However, these models lack a learning mechanism and are limited to tasks where participants have prior knowledge of the categories. To bridge the gap between cognitive and biological models, we propose a biologically plausible Spiking Neural Network (SNN) model for decision-making that incorporates a learning mechanism and whose neurons activities are modeled by a multivariate Hawkes process. First, we show a coupling result between the DDM and the Poisson counter model, establishing that these two models provide similar categorizations and reaction times and that the DDM can be approximated by spiking Poisson neurons. To go further, we show that a particular DDM with correlated noise can be derived from a Hawkes network of spiking neurons governed by a local learning rule. In addition, we designed an online categorization task to evaluate the model predictions. This work provides a significant step toward integrating biologically relevant neural mechanisms into cognitive models, fostering a deeper understanding of the relationship between neural activity and behavior.
14 June 2026
Read appraisal →JMIR mental health
Prevalence and Predictors of Self-Reported Adverse Experiences in Digital Meditation Training: 2 Randomized Controlled Trials
BACKGROUND: Digital meditation-based interventions (MBIs) reach vast global audiences with millions of active users, yet concerns persist about the frequency and nature of adverse experiences (ie, AExs) occurring during meditation training. Some researchers have argued that AExs are substantially underdetected and reflect iatrogenic harm caused by meditation (ie, adverse effects [AEfs]). Others contend that these experiences largely reflect common stressors that would be experienced without meditation. These competing perspectives underscore the need for further research, particularly in the context of digital MBIs, the most widely used form of meditation training. OBJECTIVE: This study examined the prevalence, predictors, and subjective evaluations of AExs during a digital MBI and tested whether reported experiences may be caused by meditation practice via comparisons between meditation-exposed and nonexposed participants. METHODS: Data were drawn from 2 trials of the Healthy Minds Program. Exploratory study 1 (n=315) consisted of a sample of distressed US undergraduate students to estimate the prevalence of AExs and identify baseline predictors. Preregistered confirmatory study 2 (n=594) sampled distressed US adults from all 50 states to replicate findings from study 1 and to examine participants' subjective evaluations of AExs. Study 2 additionally compared AEx rates between participants who did and did not complete guided meditations to assess whether AExs could be caused by meditation exposure. Study 3 (n=87) used qualitative methods to analyze study 1 participants' responses to an open-ended question regarding their strategies for coping with AExs. RESULTS: In studies 1 and 2, 27.9% (88/315) and 10.1% (40/396) of participants, respectively, reported at least one AEx during the study period, with 6.7% (21/315) and 3% (12/396) reporting functional impairment, largely aligning with previous research. Critically, in study 2, rates of AExs did not significantly differ between participants who did and did not complete guided meditations, suggesting that these experiences were not caused by meditation practice. Higher baseline depression, anxiety, loneliness, experiential avoidance, and perceived barriers to meditation predicted more frequent AExs. In studies 1 and 2, 89.8% (79/88) and 90% (36/40) of participants who reported AExs, respectively, indicated that they were glad to have learned to meditate. Qualitative analyses showed that participants used diverse coping strategies, often using skills learned through the Healthy Minds Program. CONCLUSIONS: AExs were relatively common but occurred at comparable rates among participants who did and did not meditate, challenging claims that such experiences were caused by meditation practice in distressed individuals. Although a small subset of participants reported some degree of functional impairment, most evaluated their AExs as tolerable and described their overall MBI experience as positive. Together, these findings highlight the importance of distinguishing AExs that likely reflect epiphenomena of preexisting distress or symptoms from iatrogenic harm attributable to MBIs.
14 June 2026
Read appraisal →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 →JMIR formative research
Digital Health Monitoring and Intervention Suite for Stress in Frontline Nurses: Prospective Cohort Trial
BACKGROUND: Stress among health care workers (HCWs) contributes to burnout, workforce attrition, and adverse patient outcomes. Although virtual reality (VR), psychoeducation, ecological momentary assessments (EMAs), and wearables have independently shown promise in stress research, no integrated digital suite has combined controlled stress induction, intervention delivery, and longitudinal real-world monitoring in HCWs. OBJECTIVE: This study aimed to evaluate the feasibility, engagement, and preliminary effectiveness of a multimodal Digital Health Monitoring and Intervention suite for Stress framework integrating VR simulation, psychoeducation, EMAs, and wearable biometrics. We examined (1) the impact of VR simulation and psychoeducation on stress outcomes and (2) associations between physiological and self-reported mental health outcomes. METHODS: Ninety-nine nurses (mean age 33.7, SD 8.9 yr, 87% female) were enrolled in 2023. We conducted a single-arm prospective cohort study (NCT05923398). Using convenience sampling, participants were recruited from social media advertisements, flyers, and email notices distributed through professional listservs. Participants completed ≥2-week baseline monitoring, a single VR session (2 runs separated by a brief psychoeducation intervention), and 12-week follow-up. In-VR stress was assessed using the Subjective Units of Distress Scale (SUDS) and 4-item Moral Injury Outcome Scale (MIOS-4), with synchronous heart rate variability. Longitudinal outcomes included weekly and biweekly EMAs alongside 70 wearable-derived features. Paired t tests, aligned rank transform ANOVA, and Pearson correlations informed study objectives, with P values adjusted for multiple comparisons. Qualitative content analysis classified emotional responses during and after VR. RESULTS: VR significantly increased subjective stress across checkpoints in both runs, with attenuation in Run B relative to Run A (all P<.001). No significant heart rate variability differences were observed between runs (P=.15). During VR, 92% (91/99) of participants felt stressed, 36% (36/99) reported anxiety or nervousness, and 51% (50/99)-78% (77/99) endorsed anger, guilt, shame, and/or betrayal. Most (59/99, 60%) HCWs returned to an emotional baseline post-VR, although 12% (12/99) reported lingering distress. Immediate reliable improvements in anger, guilt, shame, and/or betrayal occurred for 50% (50/99)-75% (74/99) of participants post intervention. Anxiety (mean -0.53, SD 2.34; P=.03) and stress (mean -3.05, SD 11.35; P=.01) decreased 2 weeks post intervention, but were not sustained at 12 weeks. Increased sleep restlessness was the only wearable feature showing significant changes (mean 2.46%, SD 5.43; Padj<.001). In-VR stress correlated with 12-week real-world stress (SUDS: r=0.57-0.58; MIOS-4: r=0.58-0.61; all P<.01). Data completion exceeded 90%, with 71% achieving full compliance. CONCLUSIONS: This study moves beyond single-tool interventions to demonstrate the feasibility and preliminary effectiveness of an integrated, multimodal stress platform within a single coordinated framework. This trial demonstrates high engagement, short-term symptom responsiveness, ecological validity, and emotional safety. The framework provides a scalable model for proactive stress identification, skills training, and implementation in high-risk occupational settings. Randomized controlled trials are needed to establish sustained efficacy and optimize deployment for real-world implementation.
13 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 →BMC geriatrics
Components and effects of psychosocial support interventions for relatives of patients with delirium in hospitals, systematic review
BACKGROUND: Relatives of patients with delirium are often confronted with patients' challenging behaviours such as hyperactivity, aggression, or hallucinations. As a result, feelings of anxiety and depression episodes have been described. The aim is to identify psychosocial support intervention for relatives of patients with delirium in hospitals, describe components and effects, and identify research gaps. METHODS: Systematic review based on PRISMA, including psychosocial support intervention studies addressing burden and support needs of adult relatives of hospital patients with delirium. We searched the databases Medline, Web of Sience, CINAHL, Livivo, PsycINFO, and Cochrane Library in 07/2024 and reference lists. We assessed reporting using TIDieR and CReDECI2 and methodological quality using ROB-1 and the JBI tool for quasi-experimental studies. We performed narrative syntheses. RESULTS: We identified eight intervention studies addressing relatives of patients with delirium in different settings. Intervention components included provision of information using flyers, videos, web-sites, or telephone calls, and mentoring in the intensive care unit. Reporting of interventions was mostly poor. No study reported a process evaluation. Four of six studies showed a significant difference in relatives' delirium knowledge. For relatives' psychological outcomes, no clear effects were shown. Overall, study quality was limited. CONCLUSIONS: More effectiveness studies are needed with better reporting of intervention components, ideally addressing psychological aspects. Feasibility and acceptance of new interventions should be described within accompanying process evaluations. TRIAL REGISTRATION: https://osf.io/7jbsk .
13 June 2026
Read appraisal →JMIR formative research
Usability, Relevance, and User Engagement of a Government-Sponsored and Community Co-Designed Digital Mental Health Website During the COVID-19 Pandemic: A Pilot Study on Latino/a/x Adults
BACKGROUND: Latino/a/x adults have higher rates of unmet mental health needs than other racial and ethnic groups. One promising solution to help bridge this gap in care is digital mental health tools. Digital tools, such as self-help websites, have demonstrated the ability to enhance mental health literacy, reduce stigma, and improve mental health symptoms. Despite the potential benefits, engagement remains a critical challenge, and there has been a large oversight of unique considerations for Latino/a/x adults as end users. OBJECTIVE: Guided by the Technology Acceptance Model and the Behavioral Model for Vulnerable Populations, the study's overarching objective is to characterize within-group variation of Latino/a/x adults' engagement with a government-funded, prevention-focused mental health website that was co-designed with community partners during the COVID-19 pandemic. METHODS: The Together for Wellness/Juntos Por Nuestro Bienestar (T4W/Juntos) website offered free digital mental health resources to help Californians cope with the COVID-19 pandemic. A pilot evaluation of the website involved baseline and 4-week follow-up surveys about demographics, behavioral health needs, and overall website user experience. This current subanalysis focused on a stratified sample of Latino/a/x adult participants (baseline N=131; baseline and follow-up n=68). The baseline sample was mostly female (106/130, 81.5%); 66.9% (87/130) preferred to use the website in English and 30% (39/130) preferred Spanish. Behavioral health needs were assessed using the Patient Health Questionnaire-2, Generalized Anxiety Disorder 2-item scale, and a COVID-19 stressors checklist. We measured usability, comfort using the website, relevance of the website, and past-month use of resources. Data were analyzed using ordered and standard logistic regression methods. RESULTS: Latino/a/x adults who preferred using the website in English (odds ratio [OR] 13.76, P<.001) compared with Spanish were more comfortable using the website. Compared with adults aged 18-30 years, adults aged 41-50 years had significantly lower odds of agreeing that the website was easy to use. Sensitivity analyses revealed that participants who found the website easier to use (OR 2.22, P=.001) and those with greater behavioral health needs (OR 1.22, P=.045) were more likely to perceive the website's topics as relevant. Participants with higher behavioral health needs at baseline were more likely to use the website and engage with resources for anxiety or stress at follow-up (OR 1.42, P=.047). CONCLUSIONS: This study addresses gaps in understanding Latino/a/x adults' experiences with a prevention-focused mental health website. The language-based disparity in comfort highlights the need to significantly improve the user experience for Latino/a/x Spanish speakers. Still, the website can be a helpful resource for Latino/a/x adults with high behavioral health needs, bridging a critical gap in support. A collaborative approach to developing resource-rich websites with trusted community organizations is vital for effectively reaching Latino/a/x communities and tailoring resources to address their unique needs.
12 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 →PLoS computational biology
A prototype-augmented graph representation learning framework for identifying brain disorder-associated genes and facilitating drug repurposing
Many genetic loci were identified as associated with neuropsychiatric disorders and neurodegenerative disorders by Genome-wide association studies (GWAS). How these loci impact these diseases is unclear. Advances in deep-learning approaches and multi-omics data have the potential to link GWAS findings with disease mechanisms. Here, we proposed the Multi-omics Graph Transformer Network (MOGT), a semi-supervised graph neural network that leverages graph representation learning to model biological networks derived from multi-omics data to predict disease-associated genes. MOGT outperforms the current approaches in disease gene prediction for two psychiatric disorders and three neurodegenerative/neurological diseases. High-risk genes (HRGs) for Parkinson's disease (PD) predicted by MOGT were used to drug discovery by integrating with the CMAP database. Finally, 10 drugs were identified as potential candidates. Among them, the effect of drug UK-356618 was experimentally verified in a primary neuron model, showing that UK-356618 reversed the abnormal expression of PD-associated genes and improved the cell-level phenotypes of PD. Together, these results indicate that MOGT can be used to identify HRGs for brain disorders, and these predicted HRGs provide high-level insights into the mechanisms and treatments of brain disorders.
1 June 2026
Read appraisal →Journal of medical Internet research
Daily Stress and Heart Rate Variability Among Mindfulness Meditation Practitioners: mHealth Observational Study
BACKGROUND: Mindfulness meditation has been reported to reduce stress and enhance well-being. However, its effects on heart rate variability (HRV)-a physiological marker of stress-remain underexplored. OBJECTIVE: This study aimed to examine how meditation practice is associated with subjective stress, HRV, and their interaction, using mobile health technologies. METHODS: This 3-week observational study included 90 participants-19 meditation practitioners (meditation group), 32 recreational runners as an active control group characterized by lower stress and higher HRV (running group), and 39 individuals without regular meditation or exercise habits (control group). HRV was continuously recorded using Garmin smartwatches. Subjective stress levels and activity states were assessed 3 times daily through a smartphone-based experience sampling method, yielding a total of 4557 responses (mean 50.6, SD 22.8 per participant). From the meditation group, start and end times of 632 daily meditation sessions (mean 33.3, SD 18.3 per participant) were also collected via the app. Standardized questionnaires on stress and related measures were administered at the end of the study period. RESULTS: The questionnaire survey confirmed that stress levels were significantly lower in both the meditation and running groups compared with controls (median Perceived Stress Scale scores: meditation 21, IQR 17-24; running 22, IQR 19-25; control 25, IQR 21-30; Kruskal-Wallis P=.02; adjusted Wilcoxon P=.05 and .04, respectively). Smartwatch-derived HRV (root-mean-square of successive differences [RMSSD]) was elevated in the running group relative to controls (median 47.0, IQR 44.0-54.2 and median 42.0, IQR 34.2-47.8, respectively; P<.001), whereas no significant difference was observed between the meditation and control groups (median 40.8, IQR 35.5-44.4 and median 42.0, IQR 34.2-47.8, respectively). Bayesian analysis of the experience sampling method data indicated that higher subjective stress levels were associated with a concurrent RMSSD reduction of -2.24 (95% credible interval -3.97 to -0.26) milliseconds. Although this pattern was consistent across groups, the steeper decline of -3.94 (95% CI -7.04 to -0.74) milliseconds was observed only in the running group, likely reflecting their elevated baseline HRV. Additionally, Bayesian modeling of 632 logged meditation sessions revealed an acute RMSSD increase of +4.68 (95% CI 2.96 to 6.38) milliseconds during meditation, with effects maintained for at least 30 minutes post practice. CONCLUSIONS: Although HRV among meditation practitioners did not appear elevated in overall daily life, the ability to increase HRV at arbitrary timings, along with the prolonged residual effect, may correlate with stress reduction. This hypothesis requires further exploration with adequate controls. Despite the preliminary nature of this study due to its limited sample size, our findings highlight the potential of mobile health-based methodologies to capture stress and HRV dynamics in real-world settings.
1 June 2026
Read appraisal →Lancet (London, England)
Comparison of [18F]flortaucipir and [18F]MK6240 for the detection of tau pathology in Alzheimer's disease (HEAD): a multicentre, prospective, cross-sectional, within-participant study
BACKGROUND: Tau PET imaging has emerged as a critical biomarker for Alzheimer's disease, informing diagnosis, staging, and therapeutic selection. We investigated whether PET tracer selection alters tau detection. METHODS: We conducted a prospective, multicentre, non-randomised, within-participant comparison of [18F]flortaucipir (Tauvid), currently used in clinical settings in the USA and Europe, and [18F]MK6240, an investigational tau PET tracer. Participants were recruited from eight north American sites and underwent tau PET, amyloid-β (Aβ) PET, and detailed cognitive assessments. Tau PET with both agents was acquired within a 45-day window. Coprimary outcomes were the discriminative accuracy for Alzheimer's disease-related cognitive impairment and the frequency of tau positivity in early medial temporal lobe (MTL) and late neocortical regions. The study is registered with ClinicalTrials.gov, NCT05361382. FINDINGS: Between March 2, 2022, and Aug 27, 2025, 775 individuals were enrolled, with 682 completing all procedures (373 [55%] female, 309 [45%] male; 38 [6%] aged 19-27 years, 214 [31%] aged 50-65 years, and 430 [63%] aged 65-89 years). 32 (5%) participants identified as Hispanic or Latino. 637 (93%) identified as White, 24 (4%) as Black or African American, 16 (2%) as Asian, and five (1%) as other. In addition, 49 (7%) individuals were identified as being from a rural area. [18F]MK6240 showed greater accuracy than [18F]flortaucipir in distinguishing Alzheimer's disease from non-Alzheimer's disease impairment (area under the curve 0·93, 95% CI 0·89-0·95 vs 0·86, 0·75-0·91; p<0·0001). Among the older adults, tau positivity status was concordant in 560 (87%) for MTL and 603 (94%) for neocortical regions. In cognitively unimpaired participants, [18F]MK6240 identified twice as many MTL-positive cases as [18F]flortaucipir (n=54 [15%] vs n=23 [6%]). Prevalence ratio in Aβ-positive was 2·43 (95% CI 1·50-3·94; p=0·0003), identifying 23 additional cases per 100. Among discordant cases, 75 (89%) were [18F]MK6240-positive only and had higher Aβ burden (p<0·0001), APOEε4 frequency (p<0·0001), and cognitive impairment (p=0·0043) than those negative on both tracers. Neocortical tau positivity was more frequent with [18F]MK6240 than with [18F]flortaucipir in cognitively impaired individuals (80 [28%] vs 46 [16%]). Prevalence ratio in Aβ-positive was 1·74 (95% CI 1·32-2·29; p<0·0001), identifying 15 additional mild cognitive impairment and 21 dementia cases per 100. INTERPRETATION: Tau PET tracer selection influences the frequency of detection of tau pathology across the ageing and Alzheimer's disease spectrum. Compared with [18F]flortaucipir, [18F]MK6240 identified more individuals with tau pathology in cognitively unimpaired and cognitively impaired individuals, with direct implications for patient stratification in clinical trials and more precise guidance for therapeutic decision-making. FUNDING: National Institute on Aging.
31 May 2026
Read appraisal →Medicine
Predicting depressive symptoms in older adults with hearing impairment development and validation of NHANES-based machine-learning models: A cross-sectional study
Depressive symptoms frequently co-occur with hearing impairment in older adults, yet available prediction tools for this high-risk subgroup remain limited in practicality and applicability. We developed and internally validated prediction models for clinically relevant depressive symptoms among older adults with hearing impairment. We analyzed cross-sectional data from 5 National Health and Nutrition Examination Survey cycles (2005-2006, 2009-2010, 2011-2012, 2015-2016, and 2017-2018). Adults aged ≥60 years with hearing impairment were identified using the 2021 World Health Organization criterion (better-ear pure-tone average at 0.5/1/2/4 kHz ≥20 dB). Depressive symptoms were defined as a Patient Health Questionnaire-9 score ≥10. Candidate predictors included sociodemographic factors, comorbidities, lifestyle behaviors, and sensory/functional limitations; missing covariates were handled using multiple imputation. Participants were randomly split into training and validation sets (7:3). Predictors were selected using the Boruta algorithm followed by least absolute shrinkage and selection operator regression, yielding 6 predictors (age, poverty-income ratio, self-rated health, sleep disorder, memory difficulty, and physical functional limitation). Logistic regression and 6 machine-learning algorithms were developed. Model performance was assessed by discrimination (area under the receiver operating characteristic curve [AUC]), calibration (calibration curves and Brier score), and clinical utility (decision-curve analysis). The analytic sample included 2123 participants, of whom 154 (7.25%) had depressive symptoms. In the validation set, AUCs across models ranged from 0.799 to 0.862. Logistic regression achieved the highest discrimination (AUC 0.862, 95% CI 0.800-0.911) with good calibration (Brier score 0.053) and favorable net benefit across clinically relevant thresholds. A nomogram, a dynamic nomogram, and an online calculator were developed to facilitate individualized risk estimation. Sensitivity analyses showed broadly consistent results, except for attenuated discrimination in the severe hearing-loss subgroup. A parsimonious 6-variable model demonstrated strong discrimination, good calibration, and potential clinical utility for risk stratification and targeted screening among older adults with hearing impairment. External validation and prospective evaluation are warranted before clinical implementation.
31 May 2026
Read appraisal →BMJ open
Multicentre randomised controlled trial of the Norwegian health in work service for patients with common mental disorders or musculoskeletal disorders: the Norwegian Sickness Absence Clinic (NSAC) Efficacy Study
INTRODUCTION: In many countries, a high or increasing rate of sickness absence is challenging the sustainability of present sickness absence benefit schemes. Most sickness absence is certified on the grounds of common mental disorders or musculoskeletal disorders, and substantial effort has been invested in developing interventions promoting return to work for these patients. In Norway, the Health in Work ((HelseIArbeid), HIA) clinics were established as outpatients' services within the specialised healthcare system, with the aim of improving health and supporting return to work. The HIA service admits patients with low-to-moderate anxiety/depression and/or musculoskeletal disorders. In this protocol, we describe the naturalistic multicentre randomised controlled trial Norwegian Sickness Absence Clinic Efficacy study, which aims to determine the effect of HIA on work participation and health. METHODS AND ANALYSES: The HIA outpatient service is staffed by clinical psychologists, physiotherapists, medical specialists in physical medicine and rehabilitation and employment support supervisors from the Norwegian Labour and Welfare Administration. Patients admitted to HIA have access to multidisciplinary assessment and treatment. The trial recruits' patients from five HIA outpatient clinics in Northern Norway. Patients are randomised in equal proportions to either (1) rapid HIA (assessment within 4 weeks), (2) delayed HIA (assessment within 10-14 weeks) or (3) active control, which consists of a monodisciplinary examination at HIA close to diagnosis-specific deadline for examination as suggested by guidelines (8-26 weeks). The trial commenced recruitment on 16 January 2023 and will recruit 2500 patients. The aim is to assess the effect of the HIA service, with the hypothesis that the HIA concept is superior to what resembles treatment as usual, in improving employment and preventing long-term welfare dependency. Secondary outcomes include self-reported symptoms of health problems. We also examine the effect the service has on other healthcare utilisation. To date, no research has been conducted to assess the efficacy of the HIA service. If proven efficacious, and if there is an economic case for this investment in tailored healthcare delivery, the policy implication may be implementation of the service at scale. If not, adaptations or investments into other viable paths of treatment may be considered. ETHICS AND DISSEMINATION: The study is approved by the Regional Committee for Medical Research Ethics (REC North, #122770). Results from the study will be disseminated at national and international scientific conferences, to funders and participating outpatient clinics in seminars and in peer-reviewed scientific journals. TRIAL REGISTRATION NUMBER: NCT05310695.
31 May 2026
Read appraisal →JMIR infodemiology
Early Depression Detection in Social Media: Monitoring of Individual Nighttime Dynamics and Large Language Model Analysis
BACKGROUND: Depression has become a major global public health challenge, and early intervention is critical for improving patient outcomes. Current depression detection techniques based on social media data (traditional risk detection) rely heavily on users' complete historical information, which cannot meet the timeliness requirements of early intervention. This underscores the need for early risk detection (ERD) methods emphasizing early-stage, real-time warning. However, existing ERD studies have notable limitations such as (1) they overlook temporal activity patterns hidden in posting time stamps, missing vital warning signals; and (2) they depend on static templates or resource-intensive sequence models, resulting in limited interpretability and inefficient use of early data, ultimately constraining their clinical applicability for early intervention. OBJECTIVE: This study aims to develop an efficient, reliable, and interpretable ERD model. The core objectives are to extract temporal activity patterns features from posting time stamps, thereby enriching feature dimensions for risk detection; to leverage large language models (LLMs) for improved text filtering precision and depression-related factor analysis; and to achieve accurate early detection of depression to support clinical intervention. METHODS: We propose the Monitoring of Individual Nighttime Dynamics (MIND) and LLM analysis model, which integrates two key innovations: (1) circadian activity dynamics: posting time stamps are transformed into temporal activity patterns, analyzing fluctuations in posting frequency and timing to derive sleep-related features, thereby compensating for the limitations of text-only approaches, and (2) LLM depression profiler: LLMs are used for dynamic text filtering, automatically removing irrelevant noise and focusing on potential depression-related cues. Based on LLM semantic understanding, latent depression risk factors are identified, enhancing interpretability for clinical treatment and robustness to noise. RESULTS: Experiments on the eRisk2017 (data source: Reddit [Reddit Inc]) benchmark dataset demonstrated that MIND significantly outperformed existing baseline models in early detection sensitivity, specificity, and accuracy. By combining sleep-related features with text analysis, the model achieved interpretable, traceable predictions that can support clinical treatment. ALL relevant experimental code is publicly available. CONCLUSIONS: The MIND model combines temporal activity pattern features with LLM-based text analysis, addressing the challenges of poor interpretability and inefficient use of early-stage data in existing ERD methods. It significantly enhances early detection performance, offering a new paradigm for applying social media data in ERD, thereby enabling earlier intervention and reducing the public health burden of depression.
31 May 2026
Read appraisal →Medicine
Prevalence and long-term outcomes of brain fog and cognitive impairment in individuals with long COVID: A systematic review
BACKGROUND: Long COVID is increasingly recognized as a complex multisystem condition, with brain fog and cognitive impairment emerging as some of its manifestations. Despite growing literature, the pooled prevalence, subgroup differences, and underlying mechanisms remain incompletely understood. METHODS: We systematically reviewed 47 studies (2000-2025) encompassing over 25,000 patients to evaluate the prevalence of brain fog and cognitive impairment among long COVID populations. Data were extracted on study design, patient demographics, follow-up duration, and subgroup variables including gender, hospitalization, vaccination, and geographic region. Risk of bias was assessed using the Newcastle-Ottawa Scale (NOS v9.0) and JBI checklists. Quantitative synthesis was performed with subgroup and temporal analyses, presented in forest plots and summary figures. RESULTS: The pooled prevalence of brain fog was 30% (95% CI: 28-32), while cognitive impairment was 25% (95% CI: 23-27). Female patients consistently showed higher rates compared to males (34% vs 23% for brain fog; 29% vs 21% for cognitive impairment). Community-managed patients demonstrated higher prevalence compared to hospitalized cohorts, and unvaccinated individuals had a greater burden than vaccinated ones. Temporal analyses indicated that prevalence increased with longer follow-up, suggesting symptom persistence or late manifestation. Pathophysiological explanations include neuroinflammation, microvascular injury, immune dysregulation, and psychosocial stressors. CONCLUSION: Brain fog and cognitive impairment are common, persistent, and clinically significant features of long COVID. Gender differences, vaccination status, and follow-up duration influence prevalence. Future studies should focus on mechanisms, preventive strategies, and targeted interventions to mitigate long-term cognitive sequelae.
31 May 2026
Read appraisal →JMIR medical informatics
Advancing Alzheimer Disease Prediction With Large Language Model-Based Linguistic Feature Analysis: Development and Validation Study
BACKGROUND: Alzheimer disease (AD) is a progressive neurodegenerative disorder with rapidly growing global prevalence. Early detection is critical for timely intervention; yet, conventional diagnostic methods remain costly and invasive. Speech-based assessment has emerged as a noninvasive alternative, as AD characteristically impairs linguistic abilities including fluency, coherence, and informational content. Recent advances in large language models (LLMs) offer new opportunities to extract structured linguistic features from transcribed speech for automated AD classification. However, existing LLM-based approaches often lack transparency and clinical interpretability, limiting their adoption in clinical workflows. OBJECTIVE: This study aims to investigate the influence of linguistic features extracted from transcribed speech, as analyzed by LLMs, on the accuracy and interpretability of AD prediction. METHODS: We propose a framework that leverages LLMs to analyze linguistic features extracted from transcribed speech for AD classification. Our approach focuses on 4 key aspects, including readability, fluency, richness of detail, and keyword relevance. To enhance classification accuracy, the framework integrates transcript embeddings with feature explanation embeddings, forming a comprehensive linguistic representation. We conducted extensive ablation studies to evaluate the contributions of individual features and benchmarked our framework against existing LLM-driven methodologies through pairwise explainability evaluations. Output stability was assessed across 3 independent pipeline runs. A fully local configuration (Llama 3 8B + nomic-embed-text) was tested to evaluate privacy-preserving deployment feasibility. Explainability was assessed via LLM-based pairwise comparison (Gemini-3.1-flash-lite) against the method of Bang et al across 54 correctly classified cases and by blinded evaluation from 2 neurologists. RESULTS: The proposed framework achieved a mean precision of 91.52%, a sensitivity of 91.08%, a specificity of 96.29%, and F1-score of 91.05% across 3 independent runs on the ADReSSo 2021 dataset, outperforming existing LLM-based approaches. A fully-local configuration (Llama 3 8B+nomic-embed-text, requiring no cloud application programming interface access) achieved an F1-score of 81.58%, demonstrating framework transferability to privacy-preserving deployment environments. Keyword relevance was the most influential feature (F1-score drop of 13.22 pp when removed). Explainability evaluations showed our method was preferred in 49 out of 54 cases via Gemini-3.1-flash-lite, with human experts preferring our method in 89 of 108 blinded assessments. CONCLUSIONS: These findings highlight that a structured linguistic feature analysis using LLMs provides a robust and interpretable framework for preliminary AD detection. Our approach offers a scalable and accessible solution that bridges artificial intelligence-driven text analysis with clinical applications, supporting early detection of cognitive decline through noninvasive assessment methods.
31 May 2026
Read appraisal →Lancet (London, England)
Efficacy and safety of oral semaglutide 14 mg (flexible dose) in early-stage symptomatic Alzheimer's disease (evoke and evoke+): two phase 3, randomised, placebo-controlled trials
BACKGROUND: Evidence, including animal, clinical, and real-world studies in individuals with type 2 diabetes and/or obesity, suggests reduced risk of dementia and Alzheimer's disease after GLP-1 receptor agonist exposure. The evoke and evoke+ trials aimed to investigate the efficacy and safety of oral semaglutide in individuals with early Alzheimer's disease. METHODS: evoke and evoke+ were multicentre, randomised, double-blind, placebo-controlled phase 3 trials conducted across 566 sites in 40 countries. The trials assessed the efficacy and safety of oral semaglutide up to 14 mg once daily in participants with amyloid-confirmed Alzheimer's disease, aged 55-85 years, with mild cognitive impairment or mild dementia due to Alzheimer's disease. In evoke+, participants with significant small vessel pathology were included. Participants were randomly assigned (1:1) to once-daily semaglutide 14 mg (flexible dose) or placebo for up to 156 weeks. The primary endpoint was change in Clinical Dementia Rating-Sum of Boxes (CDR-SB) score from baseline to week 104, assessed in all randomised participants. Safety was assessed in all randomised participants and reported for those receiving at least one dose of study drug. These trials were registered at ClinicalTrials.gov (NCT04777396 and NCT04777409); both trials have been discontinued due to negative clinical outcome. FINDINGS: Between May 18, 2021, and Sept 8, 2023, 9981 participants were screened, of whom 3808 were randomly assigned; 1855 in evoke (semaglutide, n=928; placebo, n=927) and 1953 in evoke+ (semaglutide, n=976; placebo, n=977). Mean age was 72·2 years (SD 7·1), and mean CDR-SB score was 3·7 (SD 1·6) at baseline. In evoke+, 54 (2·8%) participants had small vessel pathology. In evoke and evoke+, mean changes in CDR-SB score from baseline to week 104 were 2·3 (SE 0·1) and 2·2 (0·1) with semaglutide, compared with 2·3 (0·1) and 2·1 (0·1) with placebo (estimated difference -0·08 [95% CI -0·35 to 0·20], p=0·57 in evoke and 0·10 [-0·17 to 0·38], p=0·46 in evoke+). Treatment-emergent adverse events were reported in 1729 (91·2%) of 1896 participants receiving semaglutide versus 1613 (84·8%) of 1902 receiving placebo. There were five fatalities considered treatment-related by the investigators (one in the semaglutide group and four in the placebo group). INTERPRETATION: Oral semaglutide was not efficacious in slowing clinical progression in participants with early Alzheimer's disease. Safety and tolerability of semaglutide in early Alzheimer's disease is consistent with studies in other indications. FUNDING: Novo Nordisk.
31 May 2026
Read appraisal →BMJ open
Birth outcomes for women with pre-existing mental health problems: a systematic review and meta-analysis
OBJECTIVES: To synthesise evidence on the association between any diagnosed or self-reported mental health problems prior to pregnancy (pre-existing mental health problems) and birth outcomes including preterm birth (PTB), low birth weight (LBW), small for gestational age (SGA), neonatal unit (NNU) admission and mode of birth (instrumental birth, planned or unplanned caesarean section). METHODS: Systematic searches were conducted in MEDLINE, CINAHL, Embase and PsycINFO in December 2024 for studies examining the association between any pre-existing mental health problems and PTB, LBW, SGA, NNU admission and mode of birth. Only articles published in English were included with no restriction on year of publication. Two reviewers independently screened studies and extracted data. Study quality was assessed using the Newcastle-Ottawa Scale and Joanna Briggs Institute checklists. Random-effects meta-analyses were conducted to pool crude and adjusted ORs (aORs) and risk ratios (aRR) with 95% CIs. ORs and RRs were analysed separately. Between-study heterogeneity was quantified using the I2 statistic. RESULTS: Of 15 467 records screened, 33 studies met the inclusion criteria. Women with any pre-existing mental health problems had higher odds and risks of adverse birth outcomes, including PTB (aOR 1.41, 95% CI 1.27 to 1.56) (aRR 1.36, 95% CI 1.21 to 1.51), LBW (aOR 1.28, 95% CI 1.22 to 1.33) (aRR 1.32, 95% CI 1.04 to 1.68), SGA (aOR 1.27, 95% CI 1.07 to 1.51) (aRR 1.34, 95% CI 1.19 to 1.51) and NNU admission (aOR 1.44, 95% CI 1.19 to 1.74). Adjusted estimates were based on multivariable models that commonly controlled for maternal age, parity and socio-demographic factors. No consistent associations were observed between pre-existing mental health problems and mode of birth. CONCLUSIONS: Pre-existing mental health problems were associated with increased risks and odds of several adverse birth outcomes. These findings highlight the importance of early identification and targeted support for women with mental health problems before pregnancy to strengthen preconception and maternity care planning. PROSPERO REGISTRATION NUMBER: CRD42023485834.
31 May 2026
Read appraisal →Endocrine
Female sexual dysfunction in type 1 diabetes: a systematic review and meta-analysis
BACKGROUND: Female sexual dysfunction (FSD) in type 1 diabetes mellitus (T1DM) is an under-appreciated complication. Despite its profound impact on female health, including reduced quality of life and psychological distress, it remains underrepresented in major diabetes guidelines. Current data synthesis is limited, often excluding older populations or failing to compare diabetes subtypes. We aimed to collate the prevalence of FSD in T1DM, pool the risk of FSD in T1DM compared to controls, compare the risk in T1DM vs. T2DM and to investigate the impact of different variables on these effects. METHODS: PubMed/MEDLINE, Embase, Scopus, and Web of Science were searched to identify cross-sectional/cohort studies including T1DM participants. Proportion of women with FSD in T1DM versus age-matched control/T2DM population (if reported), and population characteristics were extracted. RESULTS: The pooled analysis of 18 studies (N = 2476) indicated FSD prevalence of 37% in adults with T1DM (95% CI: 30%, 43%; I2 = 89%; random-effects model). Analysis restricted to studies (n = 12) using standard FSFI cut-off ≤ 26.5 confirmed the summary estimate as 34% (95% CI: 26%, 41%; I2 = 89%). Trim and fill analysis for publication bias found higher FSD risk in T1DM versus age-matched controls (N = 10, adjusted Risk ratio 2.10; 95% CI: 1.30, 3.39; p = 0.002; I2 = 76%). The higher incidence of FSD in T1DM versus T2DM on subgroup analysis (p-interaction < 0.01) appeared to be influenced by diabetes duration (p < 0.01, R2 = 70%). Depression and diabetes control indicators were among most consistent predictors of FSD, whereas continuous subcutaneous insulin infusion had a protective role. CONCLUSIONS: FSD is prevalent in approximately one-third of women with T1DM, with a higher risk compared to those with T2DM, likely due to earlier onset and longer diabetes duration. Comprehensive care strategies that address both the physiological and psychological aspects of T1DM are warranted to improve sexual health and overall well-being in affected women.
30 May 2026
Read appraisal →BMJ global health
Systematic review and meta-analysis of interventions for mental health in extreme weather events
INTRODUCTION: Climate change leading to extreme weather events (EWEs) poses critical threat to human health, including mental well-being. As the effects of climate change are becoming more pronounced, there is a need to identify effective interventions to mitigate the mental health impacts. METHODS: The aim of this review was to synthesise the existing literature on interventions to prevent and manage mental health symptoms in people affected by EWEs. We conducted a comprehensive literature search in MEDLINE, EBSCO CINAHL and Cochrane till 7 February 2024 and in Web of Science and EMBASE till 20 February 2024. Randomised controlled trials (RCTs), quasi-experimental studies and before-after study designs were included. Our search yielded 11 083 potentially relevant papers, which were screened by titles and full texts and 33 studies were finally included in the review. We recorded the study, participants, intervention, disaster and other relevant data on Excel in duplicate. We conducted a meta-analysis separately for intervention-control and before-after studies using Review Manager 5.4. The Cochrane Risk of Bias tool II was used to assess risk of bias in RCTs and the risk of bias in non-randomised studies of interventions tool for quasi-experimental and before-after designs. Main outcomes were symptoms of post-traumatic stress disorder (PTSD), depression, anxiety, stress, general functioning and well-being. RESULTS: There was significant improvement in mental health symptoms following implementation of various interventions, leading to a decrease in PTSD symptoms (standardised mean difference (SMD) -2.92, 95% CI (-4.53 to -1.31), six intervention-control studies and SMD -0.71, 95% CI (-0.95 to -0.48), five before-after studies), depression symptoms (SMD -0.28, 95% CI (-0.53 to -0.03), three intervention-control studies and SMD -1.00, 95% CI (-1.41 to -0.59), 11 before-after studies), anxiety symptoms (SMD -0.38, 95% CI (-0.69 to -0.06), two studies and SMD -0.35, 95% CI (-0.47 to -0.23), eight before-after studies), stress symptoms (SMD -0.82, 95% CI (-1.10 to -0.53), two before-after studies), general functioning impairment (SMD -1.22, 95% CI (-1.73 to -0.70), three before-after studies) and an improvement in well-being (SMD 0.29, 95% CI (0.15 to 0.42), three intervention-control studies and SMD 0.67, 95% CI (0.25 to 1.09), two before-after studies). CONCLUSION: There are existing interventions to improve mental health after an EWE, and these should be integrated into all relevant disaster management plans.
30 May 2026
Read appraisal →The journal of ECT
Comparison of the Effects of Dexmedetomidine and Remifentanil on Seizure Duration, Hemodynamics, and Recovery Time in Electroconvulsive Therapy
OBJECTIVES: Electroconvulsive therapy (ECT) is an important treatment method that is widely used in psychiatric conditions resistant to pharmacological treatments. METHODS: This prospective, randomized study included 100 patients undergoing ECT. Group D received dexmedetomidine and propofol, while group R received remifentanil and propofol. In this study, key parameters such as seizure duration, hemodynamic changes, and recovery time were analyzed. RESULTS: The investigation revealed no statistically significant differences between the groups with regard to mean age, gender distribution, or seizure duration ( P > 0.05). Subsequent to induction, group R exhibited statistically significantly lower systolic blood pressure, diastolic blood pressure, and mean arterial pressures in comparison to group D ( P < 0.05). Conversely, heart rate, systolic blood pressure, diastolic blood pressure, and mean arterial pressure levels in group R were found to be statistically significantly higher than in group D ( P < 0.05). The recovery time in group D was found to be statistically significantly longer than in group R ( P < 0.05). CONCLUSIONS: Dexmedetomidine provides superior hemodynamic stability, while remifentanil ensures faster recovery. Dexmedetomidine has been shown to be more advantageous in terms of hemodynamic stability, while remifentanil has been demonstrated to result in shorter recovery times. Clinicians should tailor anesthetic choices based on patient profiles and therapeutic goals.
30 May 2026
Read appraisal →The Cochrane database of systematic reviews
Pharmacotherapy for post traumatic stress disorder (PTSD)
BACKGROUND: Posttraumatic stress disorder (PTSD) is a prevalent and disabling disorder. Evidence that PTSD is characterised by specific psychobiological dysfunctions has contributed to a growing interest in the use of medication in its treatment. OBJECTIVES: To assess the effects of medication for reducing PTSD symptoms in adults with PTSD. SEARCH METHODS: We searched the Cochrane Central Register of Controlled Trials (CENTRAL; Issue 11, November 2020); MEDLINE (1946-), Embase (1974-), PsycINFO (1967-) and PTSDPubs (all available years) either directly or via the Cochrane Common Mental Disorders Controlled Trials Register (CCMDCTR). We also searched international trial registers. The date of the latest search was 13 November 2020. SELECTION CRITERIA: All randomised controlled trials (RCTs) of pharmacotherapy for adults with PTSD. DATA COLLECTION AND ANALYSIS: Three review authors (TW, JI, and NP) independently assessed RCTs for inclusion in the review, collated trial data, and assessed trial quality. We contacted investigators to obtain missing data. We stratified summary statistics by medication class, and by medication agent for all medications. We calculated dichotomous and continuous measures using a random-effects model, and assessed heterogeneity. MAIN RESULTS: We include 66 RCTs in the review (range: 13 days to 28 weeks; 7442 participants; age range 18 to 85 years) and 54 in the meta-analysis. For the primary outcome of treatment response, we found evidence of beneficial effect for selective serotonin reuptake inhibitors (SSRIs) compared with placebo (risk ratio (RR) 0.66, 95% confidence interval (CI) 0.59 to 0.74; 8 studies, 1078 participants), which improved PTSD symptoms in 58% of SSRI participants compared with 35% of placebo participants, based on moderate-certainty evidence. For this outcome we also found evidence of beneficial effect for the noradrenergic and specific serotonergic antidepressant (NaSSA) mirtazapine: (RR 0.45, 95% CI 0.22 to 0.94; 1 study, 26 participants) in 65% of people on mirtazapine compared with 22% of placebo participants, and for the tricyclic antidepressant (TCA) amitriptyline (RR 0.60, 95% CI 0.38 to 0.96; 1 study, 40 participants) in 50% of amitriptyline participants compared with 17% of placebo participants, which improved PTSD symptoms. These outcomes are based on low-certainty evidence. There was however no evidence of beneficial effect for the number of participants who improved with the antipsychotics (RR 0.51, 95% CI 0.16 to 1.67; 2 studies, 43 participants) compared to placebo, based on very low-certainty evidence. For the outcome of treatment withdrawal, we found evidence of a harm for the individual SSRI agents compared with placebo (RR 1.41, 95% CI 1.07 to 1.87; 14 studies, 2399 participants). Withdrawals were also higher for the separate SSRI paroxetine group compared to the placebo group (RR 1.55, 95% CI 1.05 to 2.29; 5 studies, 1101 participants). Nonetheless, the absolute proportion of individuals dropping out from treatment due to adverse events in the SSRI groups was low (9%), based on moderate-certainty evidence. For the rest of the medications compared to placebo, we did not find evidence of harm for individuals dropping out from treatment due to adverse events. AUTHORS' CONCLUSIONS: The findings of this review support the conclusion that SSRIs improve PTSD symptoms; they are first-line agents for the pharmacotherapy of PTSD, based on moderate-certainty evidence. The NaSSA mirtazapine and the TCA amitriptyline may also improve PTSD symptoms, but this is based on low-certainty evidence. In addition, we found no evidence of benefit for the number of participants who improved following treatment with the antipsychotic group compared to placebo, based on very low-certainty evidence. There remain important gaps in the evidence base, and a continued need for more effective agents in the management of PTSD.
30 May 2026
Read appraisal →Journal of medical Internet research
Can Digital Tools Fix Bias in Mental Health Triage?
Recent advances in digital technologies hold both promise and potential pitfalls for mental health triage. In this News and Perspectives article, JMIR Correspondent Beth Rush reports on the issue of bias and the role of digital tools in the mental health context.
29 May 2026
Read appraisal →Journal of medical Internet research
Detection of Microbehavior Intervals for Predicting Mental Health: Clinically Relevant and Advanced Multimodal Temporal Analysis
BACKGROUND: Health care workers (HCWs) face sustained psychological demands that place them at heightened risk for burnout and posttraumatic stress disorder (PTSD). However, assessing psychological distress in this population remains challenging because of stigma, underreporting, and the limitations of self-report tools. Although nonverbal behaviors such as facial expressions and gaze hold diagnostic promise, most approaches overlook the fine-grained, temporal fluctuations in these signals. In this study, we focused on microbehavior intervals-brief, involuntary changes in multimodal nonverbal signals-that emerge during emotion-eliciting interviews. OBJECTIVE: This study aimed to determine whether microbehavior intervals improve the discrimination of psychological distress profiles among HCWs with symptoms of burnout and PTSD. METHODS: HCWs participated in a semistructured interview that included 5 work-related, emotionally charged questions and that was recorded via Webex (online video platform). Participants also completed validated questionnaires for burnout (Maslach Burnout Inventory General Survey 9-item) and PTSD (PTSD checklist for Diagnostic and Statistical Manual, 5th edition). Recordings were analyzed with computer vision models to generate time-series data of facial expressions, head movement, gaze, body posture, and hand gestures. An unsupervised anomaly detection model (MOMENT [a Family of Open Time-Series Foundation Models]) isolated microbehavior intervals without requiring manual labels. Features derived from these intervals were used to train a deep learning classifier that predicted 4 symptom classes of psychological distress: "moderate-severe burnout," "subthreshold-provisional PTSD," "burnout+PTSD," and "resilient." We conducted an ablation study by systematically removing one behavioral data stream at a time. Finally, we conducted an explainability analysis to characterize the features driving model predictions. RESULTS: We analyzed 258 interview recordings from 151 HCWs. Per interview, an average of 19.65 (SD 6.01) microbehavior intervals were detected, each lasting an average of 1.31 (SD 1.10) seconds. The classifier demonstrated robust performance across classes, achieving a macro- F1-score of 0.75 and a macro area under the receiver operating characteristic curve of 0.80 on held-out data. Ablation analysis showed that excluding gaze or arousal-valence signals caused the largest performance declines, particularly in recall and F1-score. The explainability analysis revealed distinct temporal patterns across symptom classes, with irregularity and variability in microbehaviors emerging as key predictors. CONCLUSIONS: Focusing on microbehavior intervals yields a scalable, interpretable, and annotation-free framework for detecting psychological distress from nonverbal signals. By moving from whole-video features to fine-grained multimodal temporal modeling, we successfully captured subtle, involuntary fluctuations in nonverbal responses to emotion-eliciting questions. This multimodal approach enables an objective, robust, and explainable assessment of psychological distress and offers a promising complement to conventional psychometric assessments.
29 May 2026
Read appraisal →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 →JAMA
The Veterans Affairs' Whole Health Approach for Chronic Pain Management: The wHOPE Randomized Clinical Trial
IMPORTANCE: The US Department of Veterans Affairs (VA) Whole Health approach was congressionally mandated in 2016 for patients with chronic pain receiving care in VA hospitals, but no randomized clinical trials have tested its benefits. OBJECTIVE: To evaluate the effectiveness of a whole health team intervention in VA patients with chronic pain compared with cognitive behavioral therapy and with usual care, and to evaluate the effectiveness of cognitive behavioral therapy compared with usual care in reducing long-term pain interference. DESIGN, SETTING, AND PARTICIPANTS: This randomized clinical trial involving 6 VA health systems in the US enrolled participants between September 18, 2020, and January 19, 2024. Final follow-up occurred on January 27, 2025. Analyses took place between April 1, 2025, and February 3, 2026. Participants were patients with chronic pain receiving VA primary care. INTERVENTIONS: Patients with chronic pain were randomized (11:11:2) to receive a whole health team intervention (n = 343), cognitive behavioral therapy for chronic pain delivered in group sessions (n = 339), or usual care (n = 82) for 12 months. The whole health team included a primary physician or nurse practitioner, a second clinician providing nonpharmacological or integrative pain care, and a coach. The team provided interdisciplinary, individualized care consistent with the VA Whole Health model to attain personal health goals aligned with patients' personal values and life goals. MAIN OUTCOMES AND MEASURES: The primary outcome was the Brief Pain Inventory interference (BPI-I) subscale score (range, 0-10 points; higher scores indicate worse interference from pain; minimal clinically important difference, 1.0) at 12 months. RESULTS: Of 764 randomized patients (mean [SD] age, 60.5 [12.3] years; 66.5% were men), 632 (82.7%) completed 12-month follow-up. At 12 months, the whole health group had significantly improved pain interference scores (from 6.6 to 4.9) compared with the cognitive behavioral therapy (from 6.4 to 5.5) (mean difference, -0.58 [97% CI, -1.11 to -0.05]; P = .02) and usual care (from 6.4 to 5.7) (mean difference, -0.77 [99% CI, -1.40 to -0.15]; P = .002) groups. At 12 months, cognitive behavioral therapy did not improve pain interference scores significantly more than usual care (mean difference, -0.19 [99% CI, -0.89 to 0.50]; P = .46). The most common adverse event was suicidal ideation, which occurred in 15.9% of patients in the cognitive behavioral therapy group, 13.7% in the whole health team group, and 13.4% in the usual care group. CONCLUSIONS AND RELEVANCE: These results support use of the whole health team approach to attain a statistically significant but small improvement in pain interference in VA patients with chronic pain. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04330365.
29 May 2026
Read appraisal →JMIR research protocols
Digital Cognitive Behavioral Therapy for Insomnia Versus Sleep Hygiene Education for Prevention of Perinatal Depression: Protocol for a Randomized Clinical Trial
BACKGROUND: Insomnia during pregnancy is a modifiable risk factor for depression. Research in nonpregnant populations and preliminary findings in a pregnant population suggest that targeting insomnia with digital cognitive behavioral therapy for insomnia (CBT-I) may prevent depression. OBJECTIVE: This study aims to evaluate the efficacy of digital CBT-I for the prevention of depression through 12 months post partum compared to that of a credible, clinically relevant comparator intervention, sleep hygiene education (SHE). This project will also provide valuable information about how and for whom CBT-I prevents perinatal depression. METHODS: The Perinatal Research for Improving Sleep and Mental Health study is a fully remote, 2-arm parallel-group randomized controlled trial designed to test the superiority of digital CBT-I versus SHE for the prevention of perinatal depression. We aimed to randomize a target sample of at least 443 pregnant participants with insomnia disorder to digital CBT-I or SHE at a 1:1 ratio. Participants randomized to digital CBT-I received SleepioRx (Big Health, Inc), which uses sleep consolidation, stimulus control, cognitive therapy, relaxation techniques, and SHE in 6 interactive modules (5-15 min each). Participants randomized to SHE received information about sleep hygiene, the impact of sleep on performance, healthy sleep habits, lifestyle influences on sleep, and creating a sleep-friendly bedroom in 6 weekly digital handouts. Data are collected at baseline, 5 weeks after randomization, 10 weeks after randomization, 36 weeks' gestation, and 3, 6, 9, and 12 months post partum. The primary outcome is depression incidence, measured using the Structured Clinical Interview for DSM-5 (Diagnostic and Statistical Manual of Mental Disorders [Fifth Edition]) Disorders, Research Version. Other outcome measures include the Edinburgh Postnatal Depression Scale, Patient Health Questionnaire-9, Columbia Suicide Severity Rating Scale, Generalized Anxiety Disorder Scale, and a birth outcomes questionnaire. Mediator measures include the Insomnia Severity Index, Consensus Sleep Diary, Structured Clinical Interview for Sleep Disorders, Perseverative Thinking Questionnaire, Dysfunctional Beliefs about Sleep Scale, Pre-sleep Arousal Scale, and Difficulties in Emotion Regulation-Short Form. RESULTS: Data collection began in November 2022 and is projected to end in December 2026. CONCLUSIONS: If study hypotheses are supported, then this project will provide a new, scalable approach for preventing perinatal depression.
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 medical education
Generative AI's Impact on the Mental Health of Medical Students: Scenario Analysis
BACKGROUND: Generative artificial intelligence (AI) is quickly changing medical education, even as medical students still face high levels of stress, anxiety, and burnout. These simultaneous trends-technological upheaval and ongoing mental health issues-bring up important questions about how future doctors will be trained and supported. Understanding how these factors might influence each other is crucial for developing resilient, future-ready medical education systems. OBJECTIVE: We carried out a foresight study using scenario analysis to examine potential futures at the crossroads of generative AI adoption and medical students' mental health. An initial environmental scan of the literature was conducted to pinpoint emerging trends and weak signals related to AI in medical education and well-being. These phenomena were categorized within a macro-meso-micro framework and analyzed through a multilevel sociotechnical change perspective. The study focused on 2 principal factors: the extent of generative AI integration into medical curricula and the availability of mental health support, as key drivers and critical uncertainties influencing future trajectories. METHODS: These dimensions resulted in 4 distinct scenarios: Analog Happiness (high support and low AI integration), Gen AI Paradise (high support and high integration), Disconnected Struggles (low support and low integration), and Gen AI Takeover (low support and high integration). Each scenario demonstrates how various institutional responses can impact students' digital readiness, psychological well-being, and professional growth. For each one, we identified the main systemic risks and suggested immediate institutional measures to address them. RESULTS: The findings suggest that technological innovation and mental health support must coevolve in medical education. Prioritizing one without the other risks producing either digitally unprepared or emotionally fragile physicians. Faculty readiness, ethical frameworks, and participatory curriculum design are critical to ensuring balanced integration. We formulated practical recommendations tailored to students, educators, and other stakeholders to guide balanced adaptation. CONCLUSIONS: Generative AI is more than just an additional tool in medical education; it is a systemic force that redefines how future physicians learn and operate. If technological change and student mental health are tackled separately, medical education risks creating graduates who are either unprepared for digital demands or mentally overwhelmed. This study highlights key systemic risks and suggests initial institutional steps to address them, providing a foresight-driven framework to assist educators and policymakers in responsible AI integration while safeguarding the well-being of future doctors.
28 May 2026
Read appraisal →PloS one
One out of four patients with pancreatic cancer experience psychological symptoms: A systematic review and meta-analysis
The death rate from pancreatic cancer is one of the highest of all cancers, with increasing incidence every year over the past decades. As the prognosis remains extremely poor, this trend brings not only a growing clinical challenge, but also a significant psychological toll for patients. In comparison to other types of cancer, our understanding of the psychological burden of such a deadly disease is still limited. Recognizing and addressing the psychological aspects of pancreatic cancer are crucial for comprehensive patient care. We, therefore, aimed to quantify the psychological burden of patients with pancreatic cancer. We conducted a systematic search (PROSPERO: CRD42022288805) of MEDLINE, Cochrane, and Embase databases to identify articles reporting on the prevalence of psychological symptoms among patients with pancreatic cancer. The symptoms examined were depression, anxiety, distress, fatigue, sleep disturbances. Study quality for the included studies was assessed using the Joanna Briggs Institute tool. Relevant data were extracted and exported for quantitative synthesis. The meta-analysis of the proportion of symptoms with a 95% confidence interval (CI) was conducted using frequentist random-effects models. Subset analyses were performed based on the severity of depression. A total of 28 articles were included in the meta-analysis and 31 in the systematic review. Our results showed that the proportion of depression (19 studies; 78,930 patients) was 0.27 (95% CI: 0.18-0.39). The proportion for anxiety (18 studies; 26,538 patients) was 0.29 (95% CI: 0.16-0.46; for distress (6 studies; 2746 patients) 0.43 (95% CI: 0.25-0.63); for fatigue (7 studies; 11 338 patients) 0.40 (95% CI: 0.21-0.63); and for sleep disturbances (3 studies; 10,645) 0.31 (95% CI: 0.06-0.77). These findings highlight the urgent need to integrate psychological care into pancreatic cancer treatment. Future research should explore sources of heterogeneity to better identify which approaches provide the most psychological benefit and tailor interventions to patient needs accordingly.
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 →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Psychological distress in cancer survivors: a population-based analysis and machine learning-based risk stratification
PURPOSE: As the number of cancer survivors increases, psychological distress has become an important issue. Using nationally representative data, we evaluated mental health outcomes among Korean cancer survivors compared with cancer-free controls and developed models to identify individuals at risk of psychological distress. METHODS: We analyzed data from the Korea National Health and Nutrition Examination Survey (KNHANES) from 2007 to 2021. Psychological outcomes were assessed using standardized questionnaires, and a composite distress outcome was constructed. Risk stratification models were developed among cancer survivors using logistic regression and machine learning algorithms, including random forest, XGBoost, LightGBM, support vector machines, k-nearest neighbors, and naïve Bayes. RESULTS: A total of 88,061 participants were included, comprising 3733 cancer survivors and 84,328 cancer-free controls. Compared with cancer-free controls, cancer survivors had higher odds of depressed mood (OR 1.33; 95% CI 1.18-1.51), suicidal ideation (OR 1.14; 95% CI 1.00-1.31), suicide planning (OR 1.91; 95% CI 1.37-2.65), and mental health counseling (OR 1.36; 95% CI 1.08-1.71). Among cancer survivors, multiple models were evaluated, with logistic regression showing the highest performance (AUROC 0.689), followed by XGBoost (0.686). In logistic regression, longer working hours, depression history, activity limitation, female sex, smoking, employment, low income, and distorted body image were independently associated with distress. SHAP analysis identified activity limitation, sex, and depression history as key factors. CONCLUSIONS: Cancer survivors experience increased psychological distress across multiple outcomes. Machine learning-based models may help identify individuals at higher risk of psychological distress, supporting risk-based assessment in survivorship care.
28 May 2026
Read appraisal →European journal of psychotraumatology
Biological and neurocognitive correlates of comorbid post-traumatic stress disorder and alcohol use disorder: a systematic review
Background: Post-traumatic stress disorder (PTSD) and alcohol use disorder (AUD) frequently co-occur, leading to greater clinical burden than either disorder alone. Despite this, little is known about the biological pathways linking these two disorders.Objective: We conducted a systematic review to synthesize evidence on molecular, genetic, neural, and cognitive mechanisms contributing to comorbid PTSD & AUD.Method: Following PRISMA guidelines, we performed a comprehensive search of five databases using PTSD-related, AUD-related, biological, and neurocognitive terms. Participants had to meet diagnostic criteria for both PTSD and AUD, and studies were required to have a comparator including controls, PTSD only, or AUD only. A critical appraisal was completed for all studies.Results: From 3904 identified papers, 14 met the inclusion criteria. Four studies examined the same molecular marker, with three papers derived from the same cohort, investigating baseline and stress-induced cortisol and adrenocorticotropic hormone, and found no differences unique to PTSD & AUD. One study linked low brain-derived neurotrophic factor and hazardous drinking to PTSD onset over 2 years following hospital admission. Genetic studies showed considerable overlap (72%) between PTSD and AUD in female twins, whereby the DRD2 A1 allele and the absence of the APOE ϵ2 allele were strongly associated with PTSD and drinking. Studies also reported lower neurometabolites, white matter integrity, and hippocampal volume in PTSD & AUD. Critical appraisal of these studies highlighted prominent selection bias (predominantly male and veterans) and limited justification of sample size.Conclusions: These findings suggest that PTSD & AUD may be characterized by distinct neurobiological alterations and genetic vulnerabilities relative to comparator groups. However, as there are currently insufficient data to support or refute these findings, this highlights the need for further research. PTSD and AUD frequently co-occur, leading to greater clinical burden than either disorder alone. Despite this, little is known about the biological pathways linking these two disorders.The current study aimed to review the existing molecular, genetic, neural, and cognitive evidence, and suggests similar impacted stress regulation across PTSD-only, AUD-only, and PTSD & AUD, yet distinct neural and genetic substrates among individuals with PTSD & AUD.Future research should include more female participants and a broader range of trauma types (currently dominated by veteran samples), and use large-scale neuroimaging and genetic datasets to help to identify mechanisms underpinning PTSD & AUD.
27 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 →Clinical and experimental dermatology
Novel and existing therapeutic interventions in psychogenic pruritus.
Psychogenic pruritus (PP) is a chronic itch disorder associated with psychological conditions lacking any dermatological or systemic cause. Diagnosis relies on excluding other aetiologies and identifying psychological stressors. Treatments include dermatological and psychiatric interventions such as topical steroids, selective serotonin reuptake inhibitors (SSRIs), tricyclic antidepressants, antihistamines, antipsychotics and psychotherapy. However, efficacy remains inconsistent, necessitating a systematic review of current and emerging pharmacological options. This study reviews pharmacological treatments for PP published in the past decade, evaluating efficacy and therapeutic potential. A systematic literature search was conducted using PubMed, Embase and Google Scholar (November 2014 to November 2024) following the PRISMA guidelines (PROSPERO: 1000201). In total, 1034 articles were screened by two independent reviewers. Inclusion criteria focused on human studies evaluating pharmacological treatments for PP. Seven studies met the inclusion criteria. Levels of evidence were assessed using the Joanna Briggs Institute critical appraisal checklist. SSRIs, particularly escitalopram, showed symptom improvement in case reports. Naltrexone (an opioid antagonist) led to complete itch resolution in a patient refractory to SSRIs. Gabapentin reduced pruritus severity. A neurokinin-1 receptor (NK-1R) antagonist (serlopitant) demonstrated significant efficacy in a phase II trial. Biologics such as dupilumab showed promise in chronic pruritus. Targeted therapies, including SSRIs, naltrexone, opioid pathway modulators, NK-1R antagonists and biologics, show promise in the clinical management of PP, but further research through randomized controlled trials is necessary to establish their efficacy and optimize treatment strategies.
27 May 2026
Read appraisal →Scientific reports
Depressive symptoms are a key determinant of health-related quality of life in ICU survivors with psychological distress
Survivors of critical illness frequently experience persistent impairments in health-related quality of life (HRQoL), with psychological symptoms contributing substantially to this burden. The relative contribution of co-occurring depression, anxiety, and post traumatic stress symptoms remains insufficiently understood. To address this gap, we conducted a cross-sectional analysis of pre-randomization data from the PICTURE randomized controlled trial, a multicenter study of a brief primary care-based psychological intervention for post-traumatic stress disorder symptoms following critical illness, including 319 intensive care unit survivors. Clinical, demographic, and mental health assessments were obtained after ICU discharge. Latent profile analysis, random forest modeling, and quantile regression were applied to identify determinants of HRQoL measured by the EuroQol Five-Dimension Five-Level (EQ-5D-5L) index and visual analog scale (VAS). The mean EQ-5D-5L index was 0.71 (SD 0.27; median 0.81) and the mean EQ VAS score was 60.7 (SD 19.4; median 60.0), indicating considerable overall impairment. Depression, anxiety, and post-traumatic stress symptoms showed substantial overlap and formed four distinct symptom profiles associated with specific functional impairments. Screening positive for depression on the 2-item Patient Health Questionnaire (PHQ-2) with ≥ 3 points was associated with a median reduction of -0.13 (95% CI -0.19 to -0.07) on the EQ-5D-5L index and -12.45 points (95% CI -17.93 to -6.96) on the EQ VAS, exceeding clinical and demographic predictors. These findings indicate that depressive symptoms are a major determinant of impaired health related quality of life among intensive care survivors with psychological distress and support routine brief depression screening in post-intensive care follow up.Trial registration: ClinTrials.gov: NCT03315390 (Registration date: 2017-10-20); German Clinical Trials Register: DRKS-ID: DRKS00012589 (Registration date: 2017-10-17).
26 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 →JMIR research protocols
AI-Supported, Integrative Prediction of Postoperative Delirium: Protocol for the CONFUSED Study
BACKGROUND: Postoperative delirium (POD) is a frequent and serious complication in older surgical patients, characterized by acute cognitive dysfunction and fluctuating levels of consciousness. POD is associated with prolonged hospitalization, long-term cognitive decline, reduced quality of life, and increased mortality. Despite its clinical relevance, the underlying pathophysiological mechanisms remain poorly understood, and reliable biomarkers for early prediction and prevention are lacking. OBJECTIVE: The CONFUSED study aims to identify molecular and clinical predictors of POD by integrating clinical data with proteomic, transcriptomic, and epigenetic analyses. The primary objective is to develop predictive models for POD using multimodal data. Secondary objectives include the identification of delirium-associated genes, proteins, and epigenetic signatures, as well as the exploration of patient subgroups at increased risk for POD. METHODS: CONFUSED is a prospective observational cohort study conducted at a German university hospital. Adult patients undergoing major surgery under general anesthesia will be enrolled until 100 cases of POD have been observed, which is expected to require a total sample size of approximately 200 to 300 patients. Blood samples are collected at 4 predefined time points: before premedication, immediately after surgery, and on postoperative days 2 and 5. Samples undergo comprehensive proteomic profiling, transcriptomic analysis using RNA microarrays, DNA methylation analysis, and genotyping of selected polymorphisms. Clinical data, including demographics, comorbidities, perioperative variables, medications, and delirium assessments using the Confusion Assessment Method (CAM) and CAM for the intensive care unit, are systematically recorded. Statistical analyses include univariate and multivariate methods, as well as machine learning approaches such as random forests and support vector machines, to identify relevant biomarkers and develop predictive models. The study protocol follows STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) and TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines and was approved by the responsible ethics committees. RESULTS: The study was registered in the German Clinical Trials Register (DRKS00033854) on March 18, 2024. Recruitment started in January 2024 and is ongoing at the time of manuscript submission. As of now, 135 patients have been enrolled. Sample collection and laboratory analyses are ongoing. Data analysis began in January 2026, with first results anticipated in July 2026. Final data lock is anticipated after the completion of recruitment. CONCLUSIONS: By integrating multimodal molecular data with clinical parameters and applying advanced machine learning techniques, the CONFUSED study aims to improve the prediction and understanding of POD. The results are expected to support the development of personalized preventive strategies and contribute to improved perioperative care for patients at risk of POD.
26 May 2026
Read appraisal →Journal of medical Internet research
Virtual Reality Interventions for Stress Reduction in the General Population: Systematic Review and Meta-Analysis of Randomized Controlled Trials
BACKGROUND: Increasing mental demands across multiple life domains underscore the importance of effective individual stress management to mitigate the adverse health consequences of chronic stress. Growing evidence suggests that virtual reality (VR) interventions constitute an effective approach to stress reduction. OBJECTIVE: This systematic review and meta-analysis aimed to examine and compare application areas of VR interventions for stress reduction in the general population and to identify potential predictors of effectiveness based on sample characteristics and intervention design. METHODS: Five databases (MEDLINE, CINAHL, CENTRAL, PsycInfo, and Web of Science) were systematically searched for randomized controlled trials investigating the effectiveness of VR interventions for stress reduction in the general population. Studies were included if they primarily focused on stress reduction, included a neutral control condition, and reported a validated measurement of perceived stress. Trials targeting mental disorders or those conducted in the context of medical procedures were excluded. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias using the Cochrane Collaboration's tool. Effects were synthesized using pooled standardized mean differences, and relevant predictors were evaluated through subgroup analyses and meta-regressions. RESULTS: A total of 55 relevant studies met the inclusion criteria, with 37 investigating single-session and 18 multisession interventions (ranging from 1 to 42 sessions over 2 days to 6 months). The meta-analysis included 39 studies with 4024 participants (sample sizes 24-409; mean ages 19.2-70.6 years). Intervention types included VR-based nature exposure (21), biophilic architectural elements (6), guided meditation (9), interactive tasks (4), and other approaches (1). On average, VR interventions significantly reduced perceived stress level (-0.55, 95% CI -0.70 to -0.40; P<.001; I2=76%; 95% prediction interval [PI] -1.32 to 0.23), anxiety (-0.88, 95% CI -1.23 to -0.54; P<.001; I2=87%; 95% PI -2.07 to 0.31), and depression (-0.34, 95% CI -0.47 to -0.21; P<.001; I2=0%; 95% PI -0.47 to -0.21), and enhanced positive emotion (-0.64, 95% CI -0.84 to -0.46; P<.001; I2=62%; 95% PI -1.28 to -0.02). In addition, the decrease in systolic blood pressure was significant (-0.20, 95% CI -0.37 to -0.04; P=.02; 95% PI -0.37 to -0.04). Univariate linear regression analyses identified multiple sessions (R²=15%, P=.02) as a significant predictor of VR-based stress reduction. Further analyses of technical and content-related characteristics revealed that a higher image refresh rate (t15=-2.36, 2-tailed; P=.04) was associated with stronger intervention effects. CONCLUSIONS: This review highlights the considerable potential of VR interventions for reducing psychological stress in the general population and extends recent evidence by quantifying the effects of a broad range of VR-based stress reduction approaches and, for the first time, comparing diverse intervention characteristics. VR represents a promising alternative, particularly in contexts where access to real-life nature or conventional relaxation methods is limited or resource-intensive.
26 May 2026
Read appraisal →European journal of clinical pharmacology
Ondansetron augmentation for obsessive-compulsive disorder: A systematic review and meta-analysis of randomized placebo-controlled trials
BACKGROUND: A substantial proportion of adults with obsessive-compulsive disorder (OCD) experience persistent symptoms despite optimized first-line treatment with exposure and response prevention and/or serotonin reuptake inhibitors (SRIs). Ondansetron, a selective 5-hydroxytryptamine-3 (5-HT3) receptor antagonist with plausible circuit-level relevance to cortico-striato-thalamo-cortical dysfunction, has been evaluated as an augmentation strategy. Still, evidence is dispersed across small randomized trials. METHODS: We conducted a PRISMA 2020-adherent systematic review and random-effects meta-analysis (PROSPERO: CRD420261302132). Embase (Ovid and Embase.com), MEDLINE, PubMed, and Scopus were searched from inception to 12 February 2026. Eligible studies were randomized, double-blind, placebo-controlled trials of oral ondansetron augmentation in adults (≥ 18 years) with OCD on stable background treatment. The primary outcome was change in Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) total score, pooled at prespecified acute windows (week 4, ~week 8 primary, week 12). Secondary outcomes included Y-BOCS obsession and compulsion subscales. Risk of bias was assessed using RoB 2. Random-effects models (DerSimonian-Laird) were used; Wan's method was applied when required. RESULTS: Five trials (total N = 289) were included. Ondansetron augmentation was associated with greater improvement in Y-BOCS total score versus placebo at week 4 (MD 2.63; 95% CI 0.87-4.39; I²=62%); heterogeneity resolved after excluding one influential study (MD 1.94; 0.37-3.51; I²=0%). At week 8 (four trials; N = 244), benefit was larger (MD 5.53; 3.45-7.60; I²=48%) and remained robust in sensitivity analysis (MD 6.76; 5.81-7.71; I²=0%). At week 12 (three trials; N = 200), effects persisted (MD 6.41; 4.28-8.53; I²=55%) with reduced heterogeneity after excluding one study (MD 5.06; 2.82-7.30; I²=0%). In two trials reporting subscales (N = 214), obsessions improved at weeks 4 and 8 (overall MD 2.67; 1.66-3.67; I²=0%), while compulsions showed a smaller, later signal (overall MD 1.02; 0.19-1.86; I²=0%). Safety reporting was variably detailed across trials; known QT-prolongation considerations remain relevant for off-label use. CONCLUSIONS: In randomized, placebo-controlled trials, ondansetron augmentation was associated with greater improvement in clinician-rated OCD severity than placebo over short-term follow-up, with signals emerging by week 4 and persisting through 8-12 weeks. However, these findings should be interpreted cautiously because the evidence base is small, geographically concentrated, and partly influenced by individual studies. The current evidence should therefore be considered preliminary and hypothesis-generating rather than definitive. Larger multicenter trials with standardized background SRI optimization, preregistered outcomes, and rigorous safety monitoring are required before routine clinical adoption can be considered.
25 May 2026
Read appraisal →Neuroscience
The effects of acute psychosocial stress on binge urges, inhibitory control, negative urgency and eating pathology in Binge Eating Disorder
Stress is frequently reported as a trigger for binge episodes, yet it remains unclear how subjective and physiological stress responses interact with cognitive and affective mechanisms to increase binge eating risk. The present study investigated the effects of acute psychosocial stress on binge urges, focusing on the role of state- and trait-level vulnerabilities. Twenty-eight individuals with BED were randomized to either a Stress condition (n = 14) or No-Stress condition (n = 14). Following an acute psychosocial stressor, participants completed the Stop-Signal Task to behaviourally assess inhibitory control. Self-reported mood, binge urge ratings and saliva samples for stress biomarker detection were collected at three timepoints throughout the experimental session. Results demonstrated that acute psychosocial stress elicited marked increases in total mood disturbance, without corresponding physiological stress responses and significantly impaired inhibitory control. Notably, only subjective stress responses were associated with greater binge urges, whereas physiological stress responses were not. This dissociation highlights subjective stress reactivity as a clinically meaningful mechanism of risk in BED, underscoring the potential value of interventions that target stress perception and regulation to reduce binge eating vulnerability.
25 May 2026
Read appraisal →Lancet (London, England)
Updated trends in the global prevalence and burden of mental disorders, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023
BACKGROUND: The 2023 iteration of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) estimated prevalence, incidence, and health burden for 375 diseases and injuries, including 12 mental disorders. We assess past, current, and emerging trends in the prevalence and burden of mental disorders across sexes and age groups, for 21 regions, 204 countries and territories, and by Socio-demographic Index (SDI) quintile, from 1990 to 2023. METHODS: Mental disorders included in GBD 2023 were anxiety disorders, major depressive disorder, dysthymia, bipolar disorder, schizophrenia, autism spectrum disorders, conduct disorder, attention-deficit hyperactivity disorder, anorexia nervosa, bulimia nervosa, idiopathic developmental intellectual disability, and a residual category of other mental disorders. A literature review identified epidemiological data for each disorder. These were analysed via a Bayesian meta-regression to estimate prevalence by disorder, sex, age, location, and year. Disorder-specific prevalence was multiplied by disability weights representing the severity of health loss associated with each disorder to estimate years lived with disability (YLDs). Deaths due to anorexia nervosa were assessed with a Cause of Death Ensemble modelling strategy to estimate deaths by sex, age, location, and year, and then multiplied by the standard life expectancy at age of death to estimate years of life lost (YLLs). YLDs equalled disability-adjusted life-years (DALYs) for all mental disorders except anorexia nervosa (the only mental disorder considered as an underlying cause of death in GBD), for which DALYs represented the sum of YLDs and YLLs. We presented prevalence, deaths, YLDs, YLLs, and DALYs as counts, age-specific rates per 100 000 population, and age-standardised rates per 100 000 population. FINDINGS: We estimated 1·17 billion (95% uncertainty interval 1·06-1·31) prevalent cases of mental disorders globally in 2023, equivalent to an age-standardised prevalence rate of 14 210·7 cases (12 849·5-15 940·1) per 100 000 population. These estimates represented a 95·5% (75·0-121·2) increase in prevalent cases and 24·2% (11·4-41·4) increase in age-standardised prevalence rate between 1990 and 2023. All mental disorders showed increases in prevalent cases between 1990 and 2023, while notable increases were seen in age-standardised prevalence rates for anxiety disorders, major depressive disorder, dysthymia, anorexia nervosa, bulimia nervosa, schizophrenia, and conduct disorder. There were an estimated 171 million (127-228) DALYs due to mental disorders globally across sex and age in 2023, equivalent to an age-standardised DALY rate of 2070·5 DALYs (1519·1-2750·5) per 100 000 population. Mental disorders contributed to 6·1% (4·8-7·6) of all-cause DALYs in 2023, making them the fifth leading cause of global DALYs (up from 12th in 1990). DALYs were almost entirely composed of YLDs. Mental disorders were the leading cause of YLDs in 2023 (up from second in 1990), explaining 17·3% (14·8-20·6) of all-cause global YLDs. Leading causes of mental disorder DALYs were anxiety disorders (ranked 11th among the 304 diseases and injuries at Level 4 of the GBD cause hierarchy), major depressive disorder (15th), and schizophrenia (41st). Globally in 2023, mental disorder age-standardised DALY rates were higher among females (2239·6 [1643·7-3014·1] per 100 000) than among males (1900·2 [1399·8-2510·8] per 100 000), and peaked in the 15-19 years age group (2617·3 [1850·6-3696·8] per 100 000). All locations showed increased mental disorder DALY rates in 2023 compared with 1990, ranging across countries and territories from 1302·4 (952·7-1683·7) per 100 000 in Viet Nam to 3555·8 (2661·9-4715·0) per 100 000 in the Netherlands. Across SDI quintiles, DALY rates ranged from 1853·0 (1352·1-2469·3) per 100 000 for middle SDI to 2184·1 (1606·1-2890·3) per 100 000 for high SDI. INTERPRETATION: A significant health burden was imposed by mental disorders in all countries and territories in 2023, irrespective of the health resources available. In some instances, this burden has increased over time and is unevenly distributed across populations. Stronger surveillance systems, particularly in low-income and middle-income countries, are required. Additionally, we need more coordinated and inclusive policies to reduce the burden through early treatment and prevention, tailored to sex and age differences across locations. Responding to the mental health needs of our global population, especially those most vulnerable, is an obligation, not a choice. FUNDING: Gates Foundation, Queensland Health, and University of Queensland.
25 May 2026
Read appraisal →Scientific reports
From sleep staging to spindle detection: a case study on end-to-end automated sleep analysis
Automation of sleep analysis, including both macrostructural (sleep stages) and microstructural (e.g., sleep spindles) elements, promises to enable large-scale sleep studies and to reduce variance due to inter-rater incongruencies. While individual steps, such as sleep staging and spindle detection, have been studied separately, the feasibility of automating multi-step sleep analysis remains unclear. In this case study, we evaluate whether a fully automated analysis using validated machine learning models for sleep staging (RobustSleepNet) and subsequent spindle detection (SUMOv2) can replicate findings from an expert-based study of bipolar disorder. The automated analysis qualitatively reproduced key findings from the expert-based study, including significant differences in fast spindle densities between bipolar patients and healthy controls, accomplishing in minutes what previously took months to complete manually. While the results of the automated analysis differed quantitatively from the expert-based study, possibly due to biases between expert raters or between raters and the models, the models individually performed at or above inter-rater agreement for both sleep staging and spindle detection. Our results demonstrate that fully automated approaches have the potential to facilitate large-scale sleep research. We are providing public access to the tools used in our automated analysis by sharing our code and introducing SomnoBot, a privacy-preserving sleep analysis platform.
25 May 2026
Read appraisal →Neuroscience
MSCMH-Net: A multi-scale channel-mixing hybrid network for whole-brain segmentation
Whole-brain segmentation constitutes a fundamental task in medical image analysis, providing quantitative assessment of fine-grained brain regions and serving as a cornerstone for both clinical practice and neuroscience research. Despite its importance, the task is inherently challenging given the numerous brain regions, pronounced inter-class heterogeneity, and sophisticated inter-class spatial dependencies. Accurate whole-brain segmentation requires not only precise delineation of local features but also comprehensive modeling of long-range dependencies and global contextual information. To tackle these challenges, we propose the Multi-Scale Channel-Mixing Hybrid Network (MSCMH-Net), a CNN-MLP hybrid framework integrating convolutional and MLP modules at multiple hierarchical levels. The framework leverages the strengths of CNNs to capture local features and spatial structures, while employing MLPs to model long-range dependencies and global contextual information. For integrating global and local information, a channel-mixing module incorporating an exponential moving average (EMA) fusion strategy is employed. A composite dataset of 106 brain MR scans was used, including 36 from MICCAI-2012, 30 from ADNI and 40 from OASIS. Ground truth labels were annotated and double-checked by experts. Comprehensive experiments conducted on the composite dataset validate that MSCMH-Net achieves competitive results relative to existing approaches.
25 May 2026
Read appraisal →Behavior research methods
Automating data extraction in meta-research: A multi-model benchmark in network psychometrics papers
Manual data extraction in meta-research is often tedious, time-consuming, and error-prone. In this paper, we investigate whether the current generation of large language models (LLMs) can be used to extract accurate information from scientific papers. Across the meta-research literature, these tasks usually range from extracting verbatim information (e.g., the number of participants in a study, effect sizes, or whether a study is preregistered) to making subjective inferences. Using a publicly available dataset containing a wide range of metascientific variables from 43 network psychometrics papers, we tested five LLMs (Claude 4.6 Opus, Claude 4.5 Sonnet, Claude 4.5 Haiku, GPT-5.2, and GPT-5 mini). We used an automated API-based pipeline to extract variables from the documents. This approach allows batch processing of research papers. As such, it represents a more efficient and scalable way to extract metascientific data than the default chat interface. The extraction accuracy ranged from 79.6% to 91.3% across the models. The extraction performance was generally higher for more explicit, verbatim information and worse for variables that required more complicated inference. Furthermore, most models were able to convey uncertainty in more contentious cases. We provide a comparison of the accuracy and cost-effectiveness of the individual models and discuss the characteristics of variables that are and are not suitable for automatic coding. Furthermore, we describe some of the common pitfalls and best practices of automated LLM data extraction. The proposed procedure can substantially reduce the time and costs associated with conducting meta-research.
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 →Archives of women's mental health
Moderators, mediators, and components of a standalone smartphone application for postpartum depression: secondary analysis of a randomized controlled trial
PURPOSE: The purpose of this study was to identify potential moderators, mediators, and efficacious components of a standalone app intervention on postpartum depression symptoms and functional impairment. METHODS: A total of 264 women were randomized to intervention (Motherly app) or to an active control group. We analyzed depressive symptoms assessed by the Edinburgh Postnatal Depression Scale and functional impairment by the Clinical Global Impression at post-treatment as outcomes. Various potential moderators were assessed at baseline. Potential mediation variables were the level of behavioral activation (BA) measured by the Behavioral Activation for Depression Scale - Short Form and the Response-contingent positive reinforcement (RCPR) by the Reward Probability Index, at post-treatment. Intervention component analysis was evaluated using app engagement data. RESULTS: The results indicate that intervention effects on depressive symptoms were moderated by the number of children and pediatric consultations, with stronger effects observed among mothers with fewer children and higher consultation frequency. Functional impairment was moderated by time spent with the child, with greater improvements among mothers with lower caregiving demands. Mediation analyses showed BA and RCPR mediated the effect of our intervention on depressive symptoms and functional impairment. Total and BA achievements were significantly associated with reduction in depressive symptoms. CONCLUSION: Our findings suggest that our app intervention efficacy is influenced by maternal characteristics and childcare responsibilities. BA and RCPR are key mechanisms, and higher engagement with app components produce greater symptom reduction.
24 May 2026
Read appraisal →Medicine
Nomogram and machine learning models for predicting the risk of delirium in ICU patients with NSTEMI
Delirium is a frequent and clinically consequential complication among patients admitted to the intensive care unit (ICU). Early risk stratification in patients with non-ST-segment elevation myocardial infarction (NSTEMI) remains challenging. We aimed to develop and validate prediction models for ICU delirium in NSTEMI patients using Boruta-based feature selection and machine learning (ML) approaches, and to compare ML performance with a nomogram model. We retrospectively identified adult ICU patients (≥ 18 years) diagnosed with NSTEMI in the Medical Information Mart for Intensive Care IV database, and included only those with no delirium within the first 24 hours of admission and no prior history of delirium. A total of 392 patients were included (mean age 72.2 ± 12.6 years; 64% male) and categorized into a delirium group (n = 201) and a non-delirium group (n = 191). Clinical characteristics and laboratory indices were extracted. Participants were randomly split into a training cohort (70%) and a validation cohort (30%). The Boruta algorithm was applied in the training cohort for feature selection and identification of relevant predictors. A nomogram was constructed using Boruta-selected variables. In parallel, the same features were incorporated into ML models; model discrimination and clinical utility were evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis. SHapley Additive exPlanations were used to quantify and visualize feature contributions for the best-performing model. Boruta identified 9 key variables: systolic blood pressure, serum calcium, pulse oximetry, serum sodium, creatinine, serum chloride, anion gap, blood urea nitrogen, and body temperature. In the validation cohort, CatBoost achieved the best discrimination among ML models, with an area under the receiver operating characteristic curve of 0.743 (95% CI = 0.653-0.833). The nomogram demonstrated lower discrimination (validation area under the curve = 0.61). SHapley Additive exPlanations analyses indicated that the Boruta-selected variables contributed differentially to delirium prediction, supporting model interpretability. In ICU patients with NSTEMI, a Boruta-informed CatBoost model showed moderate predictive performance and outperformed a nomogram constructed from the same predictors. This interpretable ML approach may facilitate early identification of patients at high risk of delirium and support timely preventive strategies.
24 May 2026
Read appraisal →Studies in health technology and informatics
Can NLP Detect Loneliness in Electronic Health Records? A Proof-of-Concept Study
Loneliness is clinically important but under-documented in electronic health records (EHRs), posing challenges for secondary use and computational phenotyping. This study evaluated whether natural language processing (NLP) methods can detect and classify loneliness severity from clinical notes. Patients with a loneliness survey (mild, moderate, severe) were identified, and notes within six months prior to the survey were retrieved. An expert-expanded lexicon was applied, and transformer models (RoBERTa, ClinicalBERT, Longformer) were fine-tuned for loneliness severity classification. Large language model-based summarization of social and psychiatric history was also tested as an alternative input representation. Performance was evaluated using accuracy, weighted-F1, and per-class F1. All models achieved modest accuracy (0.3 to 0.7), and struggled to identify severe loneliness, reflecting sparse and inconsistent documentation even among surveyed patients. While summarization marginally improved accuracy, gains primarily reflected mild predictions. Manual review of 100 social worker notes from severely lonely patients found explicit mentions of loneliness in only two cases, confirming that relevant documentation is exceedingly rare. These findings demonstrate that model performance is constrained by the sparse and inconsistent documentation of loneliness in EHRs, rather than by deficiencies in the modeling approach itself.
24 May 2026
Read appraisal →BMJ open
Toolkit to prompt and support stopping antidepressants in general practice: an interview study exploring patient participants' experiences in the RELEASE trial.
OBJECTIVE: To explore patient experiences with the RELEASE (Redressing long-term antidepressant use) toolkit resources, including antidepressant hyperbolic tapering plans designed to minimise withdrawal symptoms as part of the RELEASE trial's implementation evaluation to understand what aspects of the toolkit work well and identify areas where refinement could enhance future implementation. The RELEASE intervention, a multistrategy intervention codesigned with general practitioners (GPs) and patients to prompt and support stopping long-term (>12 months) antidepressants, is being evaluated in an effectiveness-implementation hybrid type-1 cluster randomised controlled trial in general practice. STUDY DESIGN: One-to-one semistructured qualitative interviews using reflexive thematic analysis to identify patterns of shared meaning. SETTING: General practice, south-east Queensland, Australia. PARTICIPANTS: Adult participants (aged 18 years or older) taking an antidepressant long-term allocated to the intervention arm of the RELEASE trial. RESULTS: 31 participants with an average duration of antidepressant use of 16.5 years (range 1-30 years) were interviewed. Seven themes were identified. Across seven themes, participants described how the RELEASE toolkit resources prompted reflection on their antidepressant use, supported informed conversations with GPs, and offered tapering plans that were both structured enough to instil confidence and flexible enough to help participants feel in control of their tapering journey. They also identified barriers, including beliefs that very small tapering doses were unnecessary, difficulty accessing the mini-doses required for hyperbolic tapering and the challenge of managing withdrawal symptoms. For those who successfully stopped, the transition was associated with a renewed sense of autonomy. Participants also offered practical suggestions to strengthen the toolkit and its broader implementation. CONCLUSIONS: The RELEASE toolkit resources played a meaningful role in prompting and supporting informed shared decision-making and guiding attempts to stop antidepressants. Clearer messaging about the potential for withdrawal symptoms and access to mini-doses for tapering are needed to support patients to safely stop antidepressants. TRIAL REGISTRATION NUMBER: ACTRN12622001379707p; Pre-results.
23 May 2026
Read appraisal →Journal of medical Internet research
Effect of Integrated Internet-Based Acceptance and Commitment Therapy and Behavioral Activation Among Ethnic Minority Young Adults With Alcohol Use Disorder in Hong Kong: Pilot Randomized Controlled Trial
BACKGROUND: Alcohol use disorder (AUD) profoundly affected ethnic minority young adults due to unique stressors, such as acculturation challenges and discrimination. Our prior studies found that experiential avoidance behavior and discrimination significantly contributed to AUD. These findings warranted third-wave interventions, such as acceptance and commitment therapy (ACT). Nonetheless, barriers to engagement with value-based activities may affect ACT efficacy. Behavioral activation (BA) may be an alternative for this shortcoming. Integrating these interventions has shown promise. However, no studies evaluated its effectiveness in this group. Therefore, evaluating the preliminary effectiveness and feasibility of this intervention was needed. OBJECTIVE: This study aimed to evaluate the feasibility measures. The secondary objective aimed to examine its effectiveness on cumulative abstinence duration, drinking days, drinks per drinking day, heavy drinking days, alcohol abstinence self-efficacy, readiness to change, psychological flexibility (Acceptance and Action Questionnaire-version II), and everyday discrimination score. METHODS: A pilot parallel randomized controlled trial was conducted. Forty young ethnic minorities who can speak English and have AUD based on DSM-5 (Diagnostic and Statistical Manual of Mental Disorders [Fifth Edition]) criteria were recruited and randomly assigned to either Acceptance and Commitment Therapy with Behavioral Activation (ACT-BA; n=20) or Treatment-as-Usual (TAU; n=20) using a computer-generated random number; assessors were blinded to group assignment. Outcomes were collected at baseline (T0) and postintervention (T1). The analysis included descriptive statistics, independent samples t test, chi-square test, and generalized estimating equations with multiple imputations. To further supplement findings, a qualitative interview was conducted. RESULTS: A total of 148 ethnic minority young adults were screened, with an eligibility rate of 38.5% (57/148). Of these, 85.9% (49/57) consented, and among them, 81.6% (40/49) were randomized to either the ACT-BA or TAU. The retention rate was 82.5% (33/40), of which 75% (30/40) completed the postintervention assessment (ACT-BA: 16/20, 80%; TAU: 14/20, 70%). The adherence rate was 81.7% (4.9/6 sessions), and participants reported no adverse effects. Finally, 40 participants (20 for each group) were analyzed. The intervention group showed a promising improvement in drinking days (B=-4.12, 95% CI -8.10 to -0.13; P=.04, d=-0.57), drinks per drinking day (B=-1.56, 95% CI -3.06 to -0.07; P=.04, d=-1.89), alcohol abstinence self-efficacy (B=11.95, 95% CI 0.10-23.81; P=.048, d=0.81), and Acceptance and Action Questionnaire-version II (B=-6.41, 95% CI -12.77 to -0.06; P=.04, d=-0.65). CONCLUSIONS: This study, unlike existing evidence, presents an innovative integration of ACT and BA delivered via an internet-based self-help format. The findings contribute to the field by providing preliminary evidence that this integrated intervention is feasible and promising for AUD. The main implication in the real world is to conduct a fully powered randomized controlled trial to further examine its effectiveness with longer follow-up to serve as a stand-alone treatment option for ethnic minorities.
22 May 2026
Read appraisal →JMIR formative research
Conceptualizing Acceptance and Knowledge as Process Variables in Internet-Delivered and Therapist-Supported Cognitive Behavioral Therapy and Acceptance and Commitment Therapy in Primary Care for Insomnia: Pilot Feasibility and Process-Oriented Randomized Controlled Trial
BACKGROUND: Internet-based interventions for insomnia show promise, but understanding the process variables, such as knowledge acquisition and psychological acceptance, is crucial for enhancing digital adherence and clinical effectiveness. OBJECTIVE: This study aimed to evaluate the feasibility, adherence, and preliminary clinical signals of 2 therapist-assisted interventions-internet-delivered cognitive behavioral therapy (iCBT) and internet-delivered acceptance and commitment therapy (iACT)-for insomnia in a primary care setting. METHODS: This was a pilot randomized controlled trial. Adults seeking help for insomnia (n=18) were recruited via primary care and randomized to either a 5-module iCBT or iACT program delivered via a secure digital platform with weekly therapist feedback. Blinding of participants and therapists was not possible due to the nature of the interventions. Primary outcomes included the Insomnia Severity Index; secondary outcomes included the 9-item Patient Health Questionnaire, 7-item Generalized Anxiety Disorder, and WHO Disability Assessment Schedule. A novel sleep knowledge test was used as a process variable. The data were analyzed using split-plot analyses of variance (intention-to-treat or last observation carried forward and complete case analysis) and nonparametric Friedman and Kruskal-Wallis tests. RESULTS: A total of 18 participants were randomized (iCBT: n=9; iACT: n=9). High attrition was observed, with only 33.3% (n=3) of iCBT and 55.6% (n=5) of iACT participants completing all modules. The iACT group demonstrated a significant within-group reduction in insomnia severity (P=.01, Friedman test), whereas iCBT results were nonsignificant (P=.10, Friedman test). No significant between-group differences were found for any clinical or process variables. Participants rated both treatments as credible (Credibility/Expectancy Questionnaire scores remained stable), though qualitative feedback indicated a need for more flexible, less burdensome content. CONCLUSIONS: This pilot study demonstrates that while internet-delivered insomnia treatments are feasible and credible in primary care, high attrition remains a significant barrier. Preliminary signals suggest that iACT may be a viable alternative to iCBT, potentially offering better adherence. Larger, fully powered pilot randomized controlled trials (estimated N=404) with refined recruitment and automated retention strategies are required to determine definitive comparative efficacy and the mediating role of sleep knowledge and acceptance.
22 May 2026
Read appraisal →PloS one
A collaborative approach to applying Natural Language Processing (NLP) to Domestic Homicide Reviews (DHRs): A study protocol
Since 2011, there has been a statutory requirement in England and Wales to conduct a Domestic Homicide Review (DHR) into any domestic abuse-related death: a multi-agency review into the death of a person aged 16 or over that appears resulting from violence, abuse or neglect from an intimate partner, family member or household member. However, analyses of large numbers of DHRs are rare. One of the core challenges is the time and effort required to analyse narrative text within reports. Doing so manually is both time-consuming and resource-intensive and is a primary reason why researchers typically focus on only a portion of the available data. Natural Language Processing (NLP)-a sub-branch of artificial intelligence that enables computers to interpret and process natural language-provides a viable and scalable alternative by offsetting much of the heavy data processing to a computer. This protocol outlines a study to assess the feasibility of applying NLP to DHRs. The study will take a collaborative approach which balances the speed and scale of automation with the embedded knowledge and expertise of practitioners. This approach helps to ensure that outputs of NLP are sensitive and transparent about the biases common within datasets on violence and abuse. Based on initial consultations, we identify the priority research questions for investigation. In addition, we outline details of an ongoing collaboration with one partner, Imkaan. The protocol describes the data access, and retrieval and analysis stages before summarising how feasibility will be evaluated. We anticipate that several challenges will emerge within this study and identify strategies for mitigation. We suggest that working with practitioners who hold deep contextual knowledge about the social realities of violence and abuse, including language, risks, and experiences, mean that tools can be developed that are accountable to communities and appropriately applied to real-world problems.
22 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 →BMJ open respiratory research
Symptom-based rehabilitation in people with post-COVID-19 condition (RELOAD study): a randomised controlled trial
RATIONALE: Post-COVID condition (PCC) is characterised by persistent symptoms that impair health-related quality of life (HRQoL); rehabilitation (REHAB) strategies remain scarce. OBJECTIVE: To investigate the effect of a symptom-based REHAB programme versus usual care on HRQoL in people with PCC. METHODS: In this single-blind randomised controlled trial, a total of 132 people with PCC were randomised to either a REHAB group or a control group, with 104 completing the trial. The REHAB group received a multimodal 3-week REHAB programme tailored to the participant's primary symptom-based group (fatigue, cognitive problems or somatic symptoms); the control group received usual care. The primary endpoint was the between-group difference in change in HRQoL, measured by the 12-item Short Form Health Survey (SF-12) physical component summary (PCS) score at 3 weeks post baseline (intervention period). Changes from baseline to a 3-month follow-up after the intervention period were also analysed as a secondary measure. Additional outcomes included mental HRQoL (SF-12 mental component summary (MCS)), depression symptoms (Patient Health Questionnaire 9 (PHQ-9)) and anxiety symptoms (Generalized Anxiety Disorder 7 (GAD-7)). RESULTS: Participants (mean age 47±12.3 years, 90.4% not hospitalised during acute SARS-CoV-2 infection, 69.2% female) in the REHAB group (n=47) showed a significantly greater improvement in PCS compared with the control group (n=57) (mean difference (Δ) 2.9 points, 95% CI 0.2 to 5.7, p=0.038) at 3 weeks post baseline. This difference remained significant at the 3-month follow-up (Δ 4.2 points, 95% CI 1.5 to 6.9, p=0.003). The REHAB group also showed significant improvements in mental HRQoL (MCS: Δ 5.7 points, 95% CI 2.0 to 9.4, p=0.003), as well as in depression symptoms (PHQ-9: Δ -2.7 points, 95% CI -4.2 to -1.2, p<0.001) and anxiety symptoms (GAD-7: Δ -2.4 points, 95% CI -3.7 to -1.1, p<0.001) at 3 weeks post baseline compared with the control group. At the 3-month follow-up, the reduction in depression symptoms remained significant, while no significant differences were observed for mental HRQoL or anxiety symptoms. CONCLUSION: A symptom-based rehabilitation programme significantly improved HRQoL in individuals with PCC after 3 weeks, with some benefits persisting at 3 months. TRIAL REGISTRATION NUMBER: NCT05172206.
22 May 2026
Read appraisal →Psychological medicine
Global prevalence of prolonged grief disorder during the COVID-19 pandemic under standardized diagnostic frameworks: A systematic review and meta-analysis
Prolonged grief disorder (PGD), recently classified in ICD-11 and DSM-5-TR, is characterized by persistent and functionally impairing grief lasting beyond 6-12 months. The COVID-19 pandemic was accompanied by widespread mortality, social isolation, disrupted mourning rituals, and social disconnection, raising concerns about a potentially high burden of PGD during the pandemic period. We conducted a systematic review and meta-analysis, following PRISMA guidelines and PROSPERO registration (CRD42023463720), to estimate PGD prevalence under standardized ICD-11 and DSM-5-TR diagnostic frameworks and to examine potential moderators during the COVID-19 pandemic. PubMed, EMBASE, and the Cochrane Library were searched from inception to October 2024. Eligible studies included adults who experienced bereavement during the pandemic and were assessed using validated PGD instruments (PG-13-R, ICG, BGQ). Random-effects models were applied to pool prevalence estimates, with subgroup and meta-regression analyses. Thirteen studies comprising 5,766 participants were included. The pooled prevalence of PGD during the pandemic period was 24% (95% CI: 13%-36%), with the highest estimates observed in China (43%, 95% CI: 33%-54%). In the overall pooled analysis, studies applying DSM-5-TR criteria yielded lower prevalence estimates than those using ICD-11 criteria (18% vs.26%, p = 0.41). Digital interventions showed no statistically significant pooled effects (Hedges' g = -0.38, 95% CI: -0.90 to 0.14). The high and geographically heterogeneous prevalence of PGD observed during the COVID-19 pandemic underscores the need to strengthen mental health surveillance, standardized assessment, and service accessibility in large-scale public health emergencies, and provides important evidence to inform population-level interventions and resource allocation strategies.
22 May 2026
Read appraisal →BMJ military health
Risk and protective factors for suicide-related outcomes among serving military personnel: a systematic review of cohort studies
INTRODUCTION: Understanding risk and protective factors for suicide-related outcomes (suicidal ideation, attempts, and deaths) among military personnel is key to the development and design of suicide prevention initiatives. Current literature has predominantly focused on ex-serving or a combination of ex-personnel and serving-personnel. Therefore, factors that may be pertinent for serving personnel are less understood. This review aims to identify risk and protective factors for suicide-related outcomes comprising serving military personnel. METHODS: The review adhered to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and was registered on PROSPERO. A systematic literature search of academic databases on military personnel and suicide from 2004 was conducted. Two independent reviewers conducted study selection. Inclusion criteria included serving personnel, cohort study design, and suicide-related outcomes. Exclusion criteria were National Guard or reservists, and studies not in English. Data on study and participant characteristics, military-specific variables, and suicide-related outcomes were extracted. All studies were critically appraised. RESULTS: A total of 53 studies were included. Most studies were published in the past 10 years, comprised army personnel from the USA, and focused on suicide attempts. Overarching risk factor categories included adverse military experiences, deployment, repeated help seeking and support, junior rank, occupation and time in service and vulnerability factors within the military context. Since many of these risk factors are inherent to military service, they may potentially be considered non-modifiable. Despite a limited number of studies on protective factors, several studies found that unit cohesion reduced the likelihood of suicidal ideation and suicide attempts. CONCLUSIONS: Suicide prevention strategies within the military may be enhanced by targeting risk factors that are potentially modifiable. Less modifiable risk factors could potentially be targeted through improved personnel management practices, particularly in preparation for and after deployment among first-year personnel. Longitudinal research programmes that identify and examine risk and protective factors for serving military personnel are needed. PROSPERO REGISTRATION NUMBER: CRD42024558183.
22 May 2026
Read appraisal →JMIR mental health
Efficacy of a World Health Organization-Guided Self-Help Intervention for Reducing Psychological Distress in Afghan Refugees: Randomized Controlled Trial
BACKGROUND: Common mental health disorders are highly prevalent among refugees. There is an urgent need to address the mental health burden in this population. OBJECTIVE: This study tested the efficacy of an individually supported self-help stress-management intervention developed by the World Health Organization-Doing What Matters in Times of Stress (DWM)-in reducing psychological distress and improving functioning among refugees in Indonesia, a major transit country in the Asia-Pacific region. METHODS: A single-blind randomized controlled trial with 303 Farsi-speaking refugees was conducted between June 2024 and June 2025. Participants with moderate to high psychological distress (Kessler Psychological Distress Scale [K10] score≥20) were randomly allocated to the facilitator-guided individual DWM condition (n=202) or a repeated assessment control condition (n=101). The primary outcome was psychological distress (K10 score) at the posttreatment assessment. Secondary outcomes were posttraumatic stress disorder symptoms, functional impairment, social functioning, and personally identified problems. RESULTS: Intent-to-treat analysis indicated that participants in the DWM condition showed greater reductions in K10 scores than those in the repeated assessment control condition (posttreatment: β=-.563, SE=0.124; P<.001; Cohen d=0.56; 1-month follow-up: β=-.447, SE=0.140; P=.002; Cohen d=0.45). Similarly, those participants in the DWM condition reported greater improvements in posttraumatic stress disorder symptoms, well-being, social functioning, functional impairment, and personally identified psychological problems. No serious adverse events were reported. CONCLUSIONS: The findings provide the first evidence for the effectiveness of DWM in reducing psychological distress and improving overall functioning among urban refugees living in a transit setting. Individually supported self-help interventions such as DWM mayoffer an effective, feasible, and scalable approach to improving mental health for refugees.
21 May 2026
Read appraisal →JMIR mental health
Automated Safety Testing and Reporting Application for Conversational Safety Monitoring of Generative AI Tools for Mental Health: Development and Validation Study
BACKGROUND: Artificial intelligence (AI)-based conversational tools are rapidly expanding within mental health care as a means of increasing access and scalability. At the same time, these systems introduce distinct safety risks arising from both user disclosures (eg, self-harm ideation) and inappropriate or inadequate AI responses. OBJECTIVE: This study aimed to develop and evaluate the Automated Safety Testing and Reporting Application (ASTRA), an external system intended to identify clinically relevant risk behaviors across entire AI-mediated mental health conversations. METHODS: ASTRA was tested on a dataset of 100 synthetic therapeutic conversations written by licensed clinicians to reflect risk behaviors and harmful responses between users and AI tools. Conversations varied in length and included both subtle and overt risk behavior examples across 8 predefined categories. Human coder consensus ratings served as the reference standard. ASTRA's classifications were evaluated across 2 prompt iterations using standard diagnostic performance metrics and agreement statistics. RESULTS: ASTRA demonstrated consistently high concordance with expert human ratings across all categories. Accuracy exceeded 0.90 for all risk behavior categories examined, with specificity uniformly high and sensitivity varying by category (range 0.55-1.00). Agreement beyond chance was substantial to almost perfect between ASTRA and human raters (κ=0.65-1.00). Detection of user self-harm indicators was particularly accurate, even in conversations where risk was expressed subtly. CONCLUSIONS: In this initial validation study, ASTRA reliably identified multiple forms of mental health-related risk behaviors at the conversation level. These findings support the feasibility of independent safety monitoring systems as a complement to AI tools used in mental health contexts and underscore the need for further evaluation using larger and real-world datasets.
21 May 2026
Read appraisal →JMIR formative research
Impact of Restriction-Resumption Protocols on Mood and Anxiety in Healthy Adults: Randomized Controlled Trial
BACKGROUND: Simple behavioral and cognitive actions can reduce symptoms of anxiety and depression. However, there is limited research investigating whether restricting those same actions increases symptoms and whether resuming those actions reduces symptoms. OBJECTIVE: The primary aim was to examine the impact of restricting, then resuming (the "restriction-resumption protocol"), 5 groups of actions called the Things You Do (TYD), on symptoms of depression and anxiety. Participant satisfaction with life and perceptions of changes in behavior and mental health were also evaluated. METHODS: In total, 70 adults were randomly allocated to an intervention group (IG) or a control group (CG). IG participants completed a 3-phase protocol over 8 weeks: a 2-week baseline period (phase A), 2 weeks of restricted TYD actions (phase B), and a 4-week resumption phase (phase C). CG participants were instructed to maintain their usual activities and routines. Primary outcomes included symptoms of depression (Patient Health Questionnaire-9), anxiety (Generalized Anxiety Disorder-7), and frequency of TYD actions. Secondary outcomes included satisfaction with life and perceived changes in mental well-being. Outcomes were measured weekly throughout the 8-week trial and again after the trial at week 9. RESULTS: Scores on outcome measures did not differ between groups during phase A and after the trial. However, by the end of phase B, IG participants showed significantly increased symptoms of depression and anxiety compared to CG participants (Ps<.001). Large within-group effect sizes were observed on the Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7 from baseline to the end of phase B (Cohen d>1.8) and from phase B to after the trial (Cohen d>1.9). Changes in satisfaction with life and evaluations of mental health corresponded with symptom changes. CONCLUSIONS: These findings demonstrate a relationship between the frequency of performing specific actions and symptoms of depression and anxiety. The restriction-resumption protocol has the potential to increase our understanding of causal mechanisms underlying common mental disorders and may lead to the development of new models of prevention and treatment. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry ACTRN12624001491550; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=388828&isReview=true.
21 May 2026
Read appraisal →JMIR formative research
Development, Feasibility, Acceptability, and Usability of an Artificial Intelligence-Powered Chatbot (Suzy) to Support Patients in Substance Use Disorder Recovery: Multiphase Study
BACKGROUND: Substance use disorder (SUD) remains a major public health crisis in the United States, with significant challenges in treatment access, retention, and workforce capacity. SUD care teams, including addiction medicine physicians and peer recovery coaches (PRCs), support patients receiving SUD treatment but face heavy workloads and burnout. Artificial intelligence (AI) innovations, particularly large language model (LLM)-based chatbots, may extend PRC support and provide patients with on-demand recovery support between clinic visits and PRC contacts. However, evidence on their development, feasibility, acceptability, and usability in addiction services remains limited. OBJECTIVE: This study describes the development, feasibility, acceptability, and usability of an AI-powered health coaching chatbot (Suzy) designed to support patients in SUD recovery. METHODS: A total of 2 clinicians, 5 researchers, and 2 technology developers led a small, multiphase pilot study. In the formative phase, they conducted focus groups and qualitative in-depth interviews with 12 health care professionals and 8 patients with substance use histories to specify chatbot functions and develop a rule-based chatbot. In phase 2, they conducted usability testing of the rule-based chatbot with 8 patients who reported substance use and completed standardized tasks, surveys, and qualitative interviews. Measures included the System Usability Scale (SUS), Net Promoter Score (NPS), and Single Ease of Use Question (SEQ). In phase 3, they developed an LLM-based chatbot co-designed and fine-tuned with PRCs and other SUD experts. RESULTS: Rule-based chatbot functions included craving management, appointment reminders, resource referrals, care team contacts, and goal setting. Usability task testing supported feasibility. In this small pilot sample, quantitative and qualitative feedback indicated acceptability and usability, with an average SUS score of 93 (benchmark 68), an NPS of 63 (benchmark 35), and a mean SEQ score of 6.5/7. Patients valued Suzy's approachable, nonjudgmental language and features that promoted accountability, self-monitoring, and 24/7 availability, while emphasizing that chatbots should supplement but not replace human support. The LLM-based chatbot development emphasized information accuracy, safety escalation protocols to mitigate risks of inappropriate chatbot responses, human-in-the-loop features, and expanded conversational flexibility and personal tailoring. CONCLUSIONS: In this pilot study, a rule-based chatbot designed to support SUD care demonstrated feasibility, usability, and acceptability. LLM-based chatbot development required more robust safety and emergency reporting features, while offering more patient-responsive conversational functions. By providing on-demand coaching, referrals, and reminders, Suzy may extend the reach of care teams, alleviate provider burden, and enhance patient engagement. Additional work is needed to understand how to best integrate Suzy into patients' recovery journeys to ensure human support remains accessible and prioritized. LLM evaluation was based on expert testing and safety review. Clinical effectiveness, including the impact on substance use, was not evaluated. Next steps include evaluating the LLM chatbot in real-world settings with larger samples and assessing its efficacy in reducing substance use.
21 May 2026
Read appraisal →BMC psychiatry
Olanzapine effectiveness on weight gain in anorexia nervosa patients: systematic review and meta-analysis
BACKGROUND: Anorexia nervosa (AN) is a complex psychiatric disorder marked by extreme caloric restriction, intense fear of weight gain, and distorted body image perception. Not only influence physical health, but it also creates serious psychological burdens. Treatment of AN is especially daunting due to its multi-dimensional nature. Recent research indicates that a pharmacological approach, such as the prescription of atypical antipsychotics like Olanzapine, may help treat both the nutritional deficiency and psychological distress related to the disorder. By allowing an individual gain weight and potentially reducing comorbid depression and anxiety, Olanzapine can be a valuable add-on to standard treatments. AIM: This study aims to investigate the effectiveness of Olanzapine in causing weight gain among anorexia nervosa patients. Based on comparisons of results in a number of clinical trials, the study aims at establishing the effect of Olanzapine on weight restoration and investigating any accompanying shift in the psychological state of the patients. METHOD: A comprehensive literature search was performed on Medline, ClinicalTrials.gov, Scopus, and the Cochrane Database of Systematic Reviews for all records from the earliest date available through to August 22, 2024. Randomized controlled trials of olanzapine treatment in anorexia nervosa were included. Studies were screened, and data were extracted. Risk of bias was assessed with the Cochrane risk-of-bias tool. Records for meta-analysis were combined in a random-effects model to calculate effect sizes, and heterogeneity was quantified with the I2 statistic. RESULT: 207 records were found, 159 of which were excluded after title and abstract screening. After full-text review, seven RCTs were found. These trials involving 152 patients assessed the impact of olanzapine on weight gain in anorexia nervosa. Meta-analysis of 4 studies (91 patients) showed a mean difference in BMI of 1.68 kg/m² (95% CI: 0.82, 4.18) in favor of olanzapine but not statistically significant (p = 0.19), with high heterogeneity (I² = 94%). Sensitivity analysis reduced the heterogeneity to 28% but not the effect size. CONCLUSION: Although olanzapine showed a numerically greater weight gain compared to control conditions, the pooled effect did not reach statistical significance and was associated with substantial heterogeneity. Therefore, current evidence remains inconclusive. Olanzapine may be considered in selected cases, particularly where psychological comorbidities are prominent, but larger and methodologically robust trials are needed before routine clinical use can be recommended. CLINICAL TRIAL NUMBER: Not Applicable.
21 May 2026
Read appraisal →JMIR formative research
Detection of Posttraumatic Stress Disorder With Rest-Activity Data: Machine Learning Approach Using Wearable and Self-Report Data
BACKGROUND: Growing evidence suggests that disruptions in rest-activity rhythms may serve as relevant markers of posttraumatic stress disorder (PTSD). Despite the emergence of machine learning methods applied to actigraphy and self-report data, few studies have used these approaches to identify individuals with clinically diagnosed PTSD. Prior work has focused on predicting probable PTSD based on self-report measures, yet discrepancies exist between clinical diagnoses and probable PTSD derived from self-reports. OBJECTIVE: This study explored whether wrist actigraphy and sleep logs could be used to accurately predict clinician-rated PTSD diagnosis and probable diagnosis of PTSD based on established self-report cutoffs (PTSD Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [PCL-5] ≥31 and ≥38) among trauma-exposed service members and veterans. We also explored which features were most strongly predictive of each outcome and whether models were able to predict PTSD diagnosis even when accounting for other mental health disorders. METHODS: Wrist actigraphy data and daily sleep logs were collected over 1 week from trauma-exposed male service members and veterans (N=36; mean age 41, SD 5.3 y). Candidate features were identified using univariate feature selection. Extreme gradient boosting models were trained using leave-one-subject-out cross-validation to predict the diagnosis of PTSD and probable diagnosis of PTSD based on 2 self-report cutoffs (PCL-5≥31 and ≥38). Performance metrics were then calculated at the person level. Linear regression was used to assess the discriminant validity of model-predicted scores and each PTSD outcome specifically, relative to other mental health diagnoses. RESULTS: Machine learning models predicting PTSD diagnosis and probable PTSD based on the PCL-5≥31 threshold demonstrated satisfactory performance in this sample. The diagnosis model achieved an area under the curve (AUC) of 0.83 (95% CI 0.61-1.00), with high accuracy (88%) and specificity (96%) and moderate sensitivity (63%). The PCL-5≥31 model yielded comparable performance (AUC=0.84, 95% CI 0.71-0.98) with balanced sensitivity (73%) and specificity (82%). For both models, a combination of subjective and objective features was the most impactful. These models were able to predict PTSD even when accounting for non-PTSD mental health diagnoses, as model-predicted scores were significantly associated with 2 outcomes: clinician-rated PTSD (B=0.19; P=.002) and probable PTSD based on a PCL-5≥31 cutoff (B=0.24; P=.003). In contrast, the model predicting probable PTSD based on the PCL-5≥38 threshold performed poorly (AUC=0.47, 95% CI 0.24-0.69), with a nonsignificant relationship between model-predicted scores and the outcome (B<0.01; P=.89). CONCLUSIONS: Both subjective and objective rest-activity features may improve the prediction of PTSD. Further research is needed to validate these findings and explore the use of integrating wearable sensor data and subjective information to support PTSD assessment.
20 May 2026
Read appraisal →Current oncology reports
Mind-Body Therapies for Depression and Anxiety Symptoms in People with Cancer: A Systematic Review with Network Meta-Analysis
INTRO: Mind-body therapies (MBTs) are promising forms of treatment for depression and anxiety symptoms in people with cancer, however, their effectiveness has not been compared. The objective of this study is to rank the effectiveness of MBTs for depression and anxiety symptoms in people with cancer. METHODS: EMBase, PubMed, Cinahl, PsychINFO, IndMED, CSI-NISCAIR, CNKI, Clinicaltrial.gov, ChiCTR, and CTRI were searched until February 2025 for randomised controlled studies in which MBTs were tested in a cancer population. Network meta-analyses were used on the selected studies to rank the reduction in depression and anxiety symptoms compared with usual care. RESULTS: A total of 182 studies which involved 16,835 participants were included. The network meta-analysis showed a statistically significant large effect compared with usual care for biofeedback (standardised mean difference, SMD = -1.18, p = 0.038), spiritual interventions (SMD = -0.98, p = 0.047), and mindfulness-based interventions (MBIs; SMD = -0.87, p < 0.001) for depression symptoms and yoga (SMD = -1.13, p < 0.001), MBIs (SMD = -1.02, p < 0.001), physical relaxation (SMD = -0.93, p < 0.001), qigong/Tai Chi (QTC; SMD = -0.90, p = 0.004) and music therapy (SMD = -0.90, p < 0.001) for anxiety symptoms. CONCLUSIONS: The MBTs that should be prioritised are biofeedback, spiritual interventions, and MBIs for depression symptoms and yoga, MBIs, physical relaxation, QTC, and music therapy for anxiety symptoms in people with cancer.
20 May 2026
Read appraisal →BMJ open
Implementation of a clinical decision support tool for postpartum depression: protocol for a prospective randomised clinical trial
INTRODUCTION: Perinatal mood and anxiety disorders affect more than one in five pregnant individuals. Despite the large percentage of individuals impacted by mood disorders, they continue to remain underdiagnosed and undertreated. Recent interventions such as risk prediction modelling offer opportunities to predict patients at risk prior to symptom onset. Leveraging technology platforms such as electronic health records allow for timely diagnosis and intervention. To date, there are no established clinical decision support (CDS) tools. We hypothesise that subjects who use a CDS aid will find them acceptable, appropriate and feasible. METHODS AND ANALYSIS: We will conduct a pilot randomised clinical trial at three Weill Cornell Medicine clinics with randomisation at the clinician level. Patients in the control arm will receive usual care, while those in the intervention arm will receive CDS. The primary implementation outcomes are the acceptability, appropriateness and feasibility. The effectiveness outcomes are mental health service utilisation, EuroQol 5 Dimensions questionnaire (EQ-5D), Edinburgh Postnatal Depression Scale and social support measured by the perceived support scale. We hypothesise that stakeholders will find the CDS acceptable, and the intervention arm will have lower mental health service needs compared with control. ETHICS AND DISSEMINATION: This study has been approved by the Weill Cornell Medicine Internal Review Board. IRB Protocol# 23-07026254. TRIAL REGISTRATION NUMBER: NCT06818734.
20 May 2026
Read appraisal →JMIR mHealth and uHealth
Multimodal Personalized Mobile Health Just-in-Time Adaptive Intervention for Occupational Stress Management: Pilot Study
BACKGROUND: As digital health solutions gain traction, there is an urgent need for effective, person-centered stress management tools for employees. Advances in wearable stress monitoring and machine learning now enable the collection of high-resolution, real-time data and the delivery of personalized interventions with respect to both timing and content. Despite this technological progress, there remains a notable paucity of mobile health (mHealth) interventions that capitalize on these capabilities to implement just-in-time adaptive interventions (JITAIs). OBJECTIVE: This pilot study aimed to examine the preliminary effects of the RELAX mHealth app, which uses a JITAI framework to reduce occupational stress, with a particular focus on initial indications of stress reduction, the influence of the app's degree of personalization on these effects, and overall user experience. METHODS: A 2-arm randomized trial was conducted with 46 employees across two phases of 3 weeks each. Participants used the RELAX mHealth app, which adapts intervention timing based on real-time stress data and tailors content according to users' preferences. Stress was assessed via multiple heart rate variability (HRV) parameters measured with wearable sensors, alongside subjective questionnaires and ecological momentary assessment (EMA) questionnaires. Usability and user satisfaction were also evaluated. In the second phase, a group randomization was implemented, with two groups of different degrees of in-app personalization. RESULTS: The findings were mixed. While selected subjective stress indicators decreased from baseline to the end of the study, one physiological measure increased. Retrospective chronic stress (P=.048) and arousal related to the most recent stress event prior to EMA entry (P=.02) were reduced; however, the low frequency (LF)-to-high frequency (HF) ratio of HRV increased (P=.03). Other stress variables, such as work-related stress, valence of daily experiences, and momentary arousal, showed no change over the course of the study. No between-group differences were observed with respect to the assigned degree of personalization. Additionally, usability increased (P<.001), whereas satisfaction decreased at a trend level (P=.05). CONCLUSIONS: Although the results provide selective preliminary indications of stress reduction with app use, the variability of outcomes raises concerns about its current practical utility. The effects are also discussed with regard to the specific design of the prototype and the study setup. Technical issues and user discomfort were noted despite increasing usability over the study period, indicating areas for improvement in the developed prototype. Given the absence of a control group and the use of interventional arms only, no definitive conclusions about effects can be drawn. Nevertheless, this study highlights both the potential and the challenges of a personalized mHealth approach to occupational stress management. Further refinement of the app and more comprehensive evaluations are needed to fully understand the impact and functional usability of this JITAI-based approach in real-world settings.
19 May 2026
Read appraisal →PloS one
Facilitating the measurement and treatment of Behavioral and Psychological Symptoms of Dementia (BPSD) and understanding caregiver burden using wearable devices in Rural Taiwan-Protocol for a dyadic feasibility pilot study
INTRODUCTION: Alzheimer's disease (AD) prevalence rises with societal ageing. In clinical care, behavioral and psychological symptoms of dementia (BPSD)-including depression, agitation/aggression, apathy, and sleep disturbance-worsen patients' quality of life and substantially increase caregiver burden, more significantly than the cognitive symptoms. Standard BPSD assessments rely on caregiver-rated questionnaires that are cross-sectional and may be biased when caregivers are themselves older adults. Device-based measures (e.g., research-grade wrist actigraphy) can provide objective longitudinal data and novel features. In parallel, therapeutic wearables may improve sleep and mood in adults, and might improve BPSD if accepted by people living with dementia. METHODS: This dyadic pilot study will recruit 20 participants (n = 10 AD patients; n = 10 caregivers) from outpatient services and affiliated day-care/dementia hubs in rural Taiwan. Participants will wear Geneactiv continuously for 8 weeks and Re-Timer ≥30 min/day for 4 weeks. Device-based data will be processed with GGIR, a well validated R-package designed for processing accelerometer data. Questionnaire assessments include Pittsburgh Sleep Quality Index (PSQI), Neuropsychiatry Inventory Questionnaire (NPI-Q), Caregiver Burden Inventory (CBI), and a semi-structured interview based on the Taiwanese version of Quebec User Evaluation of Satisfaction with Assistive Technology (T-QUEST) at prespecified timepoints. DISCUSSION: Wearable devices may facilitate the measurement and treatment of specific BPSD, as well as reduce caregiver burden. If proven feasible even in rural Taiwan where both digital and health literacy and resources are limited, this model will inform how device-based dementia care model can be considered and applied in the context of global ageing.
19 May 2026
Read appraisal →Biological psychiatry
Overview of Challenges in Brain-Based Predictive Modeling: Toward Meaningful Predictive Insights
Predictive analytics based on machine learning (ML) and artificial intelligence is a powerful tool enabling precision psychiatry and providing insights into brain-behavior relationships. However, given the mixed results observed in the field so far, making meaningful progress requires careful consideration of several key challenges to ensure the validity of models and findings, including overfitting, confounding biases, site effect harmonization, and interpretability, among others. First, we highlight limitations of cross-validation, a ubiquitous ML strategy used to prevent overfitting and obtain generalization estimates, emphasizing the risk of performance inflation and the need for independent validation. Next, we introduce different types of so-called third variables that can influence the examination of a brain-behavioral relationship of interest in different ways, using causal inference principles. We emphasize the biasing impact of confounding variables on ML models and summarize common mitigation strategies. We then discuss site-specific effects in multisite datasets, reviewing different harmonization strategies to reduce unwanted variability and site-specific noise. Finally, we explore post hoc model interpretation methods to enhance model transparency while cautioning against misinterpretation. By integrating rigorous result validation, confounder control, and interpretability techniques, researchers can ensure that ML models produce more reliable and generalizable findings and avoid spurious associations.
18 May 2026
Read appraisal →Science advances
Wireless, skin-interfaced multimodal sensing system for continuous psychophysiological monitoring-A wearable polygraph device
Accurate, continuous monitoring of psychophysiological states is central to understanding stress and autonomic dysfunction across diverse medical contexts. Current approaches such as polygraphy and polysomnography rely on cumbersome, wired sensors that limit real-world utility and burden patients, particularly vulnerable populations such as infants. Here, we introduce a wireless, skin-interfaced multimodal sensing system capable of simultaneously recording cardiac, respiratory, electrodermal, and thermal signals in a time-synchronized manner. Leveraging compact and soft designs, the technology enables unobtrusive monitoring across controlled, clinical, and naturalistic settings. Validation studies performed in parallel with gold standard systems demonstrate high fidelity in quantifying stress responses during polygraph interviews, cognitive load tasks, and cold pressor tests. In pediatric sleep studies, the data reliably identify arousals, hypopnea, and apnea while revealing disease-specific autonomic signatures in infants with Down syndrome. Real-world deployment during emergency simulation training shows that multimodal stress signatures correlate inversely with performance, underscoring translational value in medical education. Machine learning analyses across all studies confirm that multimodal features outperform single-signal approaches in detecting stress and clinical events with high sensitivity and specificity. Collectively, these findings establish the technology as a next-generation wearable platform that bridges engineering innovation and clinical practice, offering mechanistic insight and diagnostic potential in stress medicine, sleep medicine, and beyond.
18 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Effects of yoga on depressive symptoms in breast cancer patients: a meta-analysis of randomized controlled trials
BACKGROUND: While yoga is known to benefit depression in breast cancer patients, its efficacy as an adjunct to diverse treatment regimens remains unclear. This study evaluated yoga's efficacy in alleviating depression during varied breast cancer therapies and supports its clinical integration as a complementary intervention. METHODS: Following PRISMA 2020 guidelines, we systematically searched for RCTs in English and Chinese. Literature quality was assessed using the Cochrane Risk of Bias tool. Data were analyzed with Review Manager 5.4.1, including subgroup analyses by treatment type and assessment scale. RESULTS: Analysis of 21 studies showed yoga significantly reduced depression levels [SMD = -0.58, 95% CI (-0.83, -0.32), P < 0.00001]. Subgroup analyses revealed a significant combined mean difference among the surgery group [SMD: -1.04], the postoperative radiotherapy group [SMD: -2.47], and the postoperative chemotherapy group [SMD: -0.46]. The group receiving postoperative radiotherapy demonstrated a significantly greater improvement in depression. Subgroup analyses of scale-assessed effects confirmed the validity of the Beck Depression Inventory (BDI), the Center for Epidemiologic Studies Depression Scale (CESD), the Hospital Anxiety and Depression Scale (HADS), and the Zung Self-Rating Depression Scale (SDS). The HADS demonstrated a greater effect size [SMD = -1.19, 95% CI (-2.13, -0.24), P = 0.01]. CONCLUSIONS: The impact varied among those undergoing different treatment regimens. Notably, the degree of improvement in depression appeared to be greater in the postoperative radiotherapy group. HADS is a practical self-assessment scale that has demonstrated considerable improvement and is recommended for use in breast cancer patients with depression.
18 May 2026
Read appraisal →Computers in biology and medicine
LADNET: An MRI-based deep learning approach for Alzheimer's disease detection.
Alzheimer's disease (AD) is considered a leading form of dementia, found in the majority of individuals, and it accounts for the highest prevalence of all the types of dementia. It is a gradually progressing condition which can begin with mild memory impairment and may lead to the inability to engage in meaningful conversations in the future. In addition to that, it is also capable of reacting appropriately to the environment around it. According to official data, there has been an increase in reported cases of AD-related mortality in recent years, along with rising rates of the disease itself. Therefore, it is beneficial for patients to be diagnosed with AD as early as possible in order to maximize their chance of survival. In medical field, deep learning (DL) is employed in many applications, especially the identification of Alzheimer's disease, has shown considerable success. Based on the fact that these methods are capable of acquiring and extracting features from extensive datasets autonomously, they are highly suitable for analyzing intricate medical images with the aid of these methods. Design the Lightweight Alzheimer's Disease Net (LADNET) model for the current task. Accurate detection of AD using magnetic resonance imaging (MRI)-based scans is presented in this work. The model classifies the three dementia types, namely Mild Dementia (MD), Moderate Dementia (MDD), and Very Mild Dementia (VMD), while also accurately categorizing the non-demented (ND) category based on the severity of the condition. We measure the performance of the model we recommend against a publicly available Kaggle dataset that contains over six thousand images from four different types of images. LADNET model achieves 99.4% accuracy and 99% AUC, outperforming existing methods. With a lightweight design of approximately 1.2 million parameters and fast inference (4.2 ms per image), LADNET demonstrates strong potential for practical clinical deployment, where it could help extract biomarker information from conventional MRIs, potentially reducing patient burden and diagnostic costs.
17 May 2026
Read appraisal →Journal of affective disorders
Biological age acceleration associated with mental and behavioural disorders: Evidence from the UK biobank cohort.
BACKGROUND: Mental and behavioural disorders may accelerate biological ageing, but comprehensive evidence integrating observational and causal approaches is lacking. METHODS: We performed observational and Mendelian randomisation (MR) analyses. First, 502,411 UK Biobank participants (78,674 with ICD-10 Mental and Behavioural Disorders) were propensity score matched (1:1). Biological Age Acceleration (BAA = biological age - chronological age) was regressed on mental and behavioural disorder status, and robustness checks were conducted using heteroscedasticity-robust and cluster-robust standard errors. Second, two-sample MR assessed causal effects of genetic liability to six mental and behavioural disorders on four ageing biomarkers, using strong genetic instruments (F > 10) and validated through sensitivity analyses including MR-Egger, Cochran's Q, and leave-one-out tests. RESULTS: Ever having a diagnosis of a mental and behavioural disorder was associated with higher BAA (β = 0.261, 95% CI: 0.229-0.293, P < 0.001). Strongest associations were observed for disorders of psychological development (β = 0.717), organic mental and behavioural disorders (β = 0.395), and mental and behavioural disorders due to psychoactive substance use (β = 0.338). BAA increased with comorbidity count (P-trend <0.001). MR indicated potential causal effects of depression (OR = 5.12 for frailty index), insomnia (OR = 2.04 for frailty index), and anxiety (OR = 1.06 for frailty index) on accelerated ageing, with no evidence of horizontal pleiotropy (MR-Egger intercept P > 0.05). Bipolar disorder was associated with slower epigenetic ageing (OR = 0.71 for GrimAge). Alcohol use disorder increased facial ageing (OR = 1.02). Schizophrenia showed no significant associations. CONCLUSIONS: Mental and behavioural disorders are observationally and robustly linked to accelerated biological ageing, with a dose-response by comorbidity. Genetic evidence supports potential causal roles for depression, insomnia, and anxiety in promoting ageing, while bipolar disorder may relate to slower epigenetic ageing. Biological ageing should be considered in the long-term management of mental health.
17 May 2026
Read appraisal →Journal of affective disorders
Adverse events related to buspirone: a real-world pharmacovigilance study using the FAERS database
Buspirone, a widely used anxiolytic, has incompletely characterized adverse effects. This study aimed to comprehensively characterize buspirone's safety profile by a large-scale analysis of real-world data from the Food and Drug Administration Adverse Event Reporting System (FAERS) database, covering Q1 2006 to Q1 2025. We collected 1744 buspirone-related adverse event reports and analyzed them utilizing various signal detection techniques, including proportional reporting ratio (PRR), reporting odds ratio (ROR), multi-item gamma Poisson shrinker (MGPS), and Bayesian confidence propagation neural network (BCPNN). The most common adverse events were categorized into nervous system disorders and psychiatric disorders, with "dizziness" (n = 141), "anxiety" (n = 133), "drug interaction" (n = 86), "serotonin syndrome" (n = 83), and "tremor" (n = 71) being the most frequently reported. Notably, several safety issues linked to suicide exhibited considerable signals. In addition to the adverse events already listed on the package insert, our research identified unanticipated adverse events associated with improper buspirone use, including "drug abuse" (n = 43), "intentional overdose" (n = 30), and "drug level increased" (n = 17). These findings indicated a potential risk of abuse with buspirone exposure during therapeutic use. Moreover, the study detected a relatively rare but noteworthy signal associated with abnormal body weight, such as "obesity" (n = 19), which merits further attention. The median time to onset for adverse events was 10 days (IQR:16). In conclusion, this pharmacovigilance research underscored the safety hazards related to prolonged buspirone usage, confirmed known risks and identified novel safety signals. These findings enriched the awareness of buspirone's safety profile and provided evidence-based guidance in clinical settings.
17 May 2026
Read appraisal →Biological psychiatry
Multimodal Representation Learning for Parsing Biological Heterogeneity in Psychiatric Neuroimaging
For decades, psychiatric neuroimaging has searched for biomarkers of depression and other disorders, but they remain elusive in clinical practice. While the last 5 years have seen rapid progress, other large-scale correlative studies have found only small, unreliable links between brain measures and clinical symptoms. Growing evidence suggests that such limitations are not just about sample size but depend critically on how models represent data. This review traces a recent shift away from univariate methods to multivariate/multiview approaches that learn more effective representations of biological and symptom measures by flexibly learning multimodal latent representations. First, we review how linear multiview embedding methods have revealed reproducible biological depression subtypes but do not perform well in small samples or samples enriched for mild symptoms. Then, we consider newer work exploring more sophisticated representations for neuroimaging data, including deep-learning and graph-based representations, and multimodal extensions that uncover complex latent patterns that single-modality studies miss. Then, we review recent developments in foundation models, which, once trained on large corpora, can "transfer learn" readily to small clinical cohorts, potentially bringing the advantages of large-scale learning to small, privacy-limited data. Finally, we highlight emerging representation tools that treat the brain as a dynamic, stateful multivariate process. Taken together, these advances point to a future in which the value of neuroimaging will be determined not only by ever-larger sample sizes but also by data quality and by how well our algorithms capture the distributed, multimodal, and evolving nature of psychiatric disorders.
17 May 2026
Read appraisal →Psychological medicine
Effectiveness of a one-year smoking cessation intervention for people with severe mental illness: results of the KISMET cluster-randomized controlled trial
BACKGROUND: This study evaluated the effectiveness of a one-year smoking cessation intervention for people with severe mental illness compared with treatment as usual (TAU) in outpatient mental healthcare. METHODS: The KISMET study is a pragmatic cluster-randomized controlled trial conducted in 21 outpatient mental healthcare teams in the Netherlands. Eleven teams delivered the KISMET intervention comprising cognitive-behavioral and peer support, combined with optional pharmacological reatment. Ten teams participated in the TAU condition. We collected data between October 2022 and July 2024. The primary outcome was self-reported smoking cessation at 12 months, verified through exhaled carbon monoxide levels below 10 parts per million. Secondary outcomes included depression and anxiety (HADS), severity of psychotic symptoms (PANSS-6), quality of life (SF-12), disease self-management (PAM-13), lipid profile, blood pressure, body mass index, glucose level, and physical fitness. Crude and adjusted linear and multivariable logistic regression and mixed model analyses were performed. RESULTS: Eighty-nine participants were included in the KISMET intervention and 44 in TAU. Smoking cessation rates were significantly higher in the KISMET group at 3 months (OR 12.1, 95% CI 1.4 to 103.7) and at 12 months (OR 4.2, 95% CI 1.0 to 17.2) but not at 6 months (OR 1.9, 95% CI 0.5 to 6.9). No significant differences between groups were found for secondary outcomes. Dropout rates were 58% in the intervention and 32% in the TAU group. CONCLUSIONS: The KISMET intervention shows potential without signs of physical or psychopathological complications. However, results must be interpreted with the high dropout rates in mind.
17 May 2026
Read appraisal →Journal of affective disorders
Right orbitofrontal cortex rTMS targets anxiety, not depressive, symptoms in first-episode schizophrenia.
BACKGROUND: Anxiety is highly prevalent and undertreated in first-episode schizophrenia(FES), but repetitive transcranial magnetic stimulation(rTMS) data for this comorbidity are scarce. The orbitofrontal cortex(OFC) is a key anxiety-regulating node, supporting its potential as a target. This study aimed to explore 1-Hz right OFC-rTMS's symptom-specific effects on FES mood. METHODS: This is a secondary analysis of a randomized controlled trial. Participants were drug-naive FES patients randomized to the active rTMS group(n = 51) or sham group(n = 45). All completed 20 intervention sessions, 20 sessions of active OFC-rTMS or sham, with 4-week follow-up, initiating oral olanzapine(10-20 mg/day) concurrently with the first rTMS session. Mood symptoms were assessed using the 24-item Hamilton Depression Rating Scale(HAMD) and the 14-item Hamilton Anxiety Rating Scale(HAMA). Psychopathological symptoms were assessed using the Positive and Negative Syndrome Scale(PANSS). The main outcome was the changes in HAMD and HAMA scores from baseline to 2 weeks and 4 weeks. RESULTS: The active group showed greater PANSS total(t = -3.260, p = 0.002; Cohen's d = 0.672) and subscale improvements vs. the sham group. Repeated-measures ANOVA(controlling for covariates) revealed significant Time×Group interactions for HAMA total(F = 4.698, p = 0.010; partial η2 = 0.059) and psychic anxiety(F = 5.735, p = 0.004; partial η2 = 0.072), but not somatic anxiety. For HAMD, only anxiety/somatization(F = 8.397, p = 0.031; partial η2 = 0.099) and cognitive impairment(F = 6.240, p = 0.002; partial η2 = 0.076) showed interactions, with no specific effects on overall depressive symptoms. In the sham group, HAMD anxiety/somatization correlated with all PANSS subscales(r = 0.311-0.477, p < 0.05), but this correlation was absent in the active group(all p > 0.05). CONCLUSIONS: Right OFC-rTMS improves FES anxiety (not depression), supporting it as a targeted non-pharmacological option.
17 May 2026
Read appraisal →JMIR mental health
Factors Influencing the Initiation and Continued Engagement of Digital Mental Health Tools Among Adults: Theory of Planned Behavior-Informed Systematic Review
BACKGROUND: Digital mental health tools (DMHTs) offer scalable support, but engagement varies. Understanding the shapes of initiation and ongoing use is essential for effective design and implementation. OBJECTIVE: This study aims to synthesize determinants of adults' initiation and engagement with DMHTs, organized through two lenses: (1) psychological factors aligned with the theory of planned behavior (TPB) and (2) design and access features. METHODS: A systematic search of 9 databases (June 2025) identified qualitative and mixed methods primary studies reporting end-users' experiences with DMHTs. Studies were screened and reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Quality appraisal used quality assessment with diverse studies (QuADS). Data were synthesized using a framework-guided thematic approach, mapping findings to TPB constructs and complementary design and access domains. RESULTS: A total of 22 studies met inclusion criteria. Findings clustered into 2 interdependent domains. TPB constructs explained how beliefs, social expectations, and perceived control shaped decisions to start and persist with DMHTs. Design and access features frequently acted through these same pathways, especially by altering perceived behavioral control (PBC), with cost, connectivity, device constraints, and time flexibility affecting feasibility, with content design and privacy shaping perceived value and trust. Perceived fit (goals, cultural or linguistic relevance, and routine alignment) consistently influenced both initiation and continuation. Several features operated bidirectionally; depending on context, the same feature could facilitate or hinder engagement. CONCLUSIONS: Engagement with DMHTs is jointly determined by users' beliefs and the design and access conditions within which tools are offered. Implementation should pursue a dual strategy, strengthening willingness to seek support (addressing attitudes, norms, and perceived control) while engineering low-effort, trustworthy, and context-appropriate experiences. Priorities include equity-focused policies (data costs, devices, and connectivity), transparent data practices, co-design with diverse communities, and consistent, theory-informed outcome measures.
17 May 2026
Read appraisal →JMIR mHealth and uHealth
Smartphone Apps for Cannabis Cessation: Quality Assessment and Content Analysis
BACKGROUND: Over the past 2 decades, global rates of cannabis use have risen significantly, especially among young adults. This has corresponded to an increase in cannabis-related problems and hospitalizations. Thus, there has been significant interest in developing new interventions that can help facilitate cannabis cessation and reduce hospitalization rates. Specifically, mobile apps have emerged as scalable and accessible stand-alone or adjunct interventions that can help individuals with cannabis use disorders. OBJECTIVE: This study aimed to evaluate the quality of free cannabis cessation apps available on both the Apple App Store and Google Play Store, focusing on the analysis of their features, content, and adherence to evidence-based practices. METHODS: A systematic search was conducted in April 2023 using a variety of keywords. The apps were deemed eligible if they were free, available in English, accessible on both the Apple App Store and the Google Play Store, and related to cannabis cessation. Eligible apps were used for at least 1 month and were rated on the Mobile App Rating Scale by 2 reviewers. Interrater reliability was excellent, with a weighted Cohen κ of 0.893 (95% CI 0.835-0.943). RESULTS: Four apps were included in the analysis, namely, "Grounded-Quit Weed," "Quit Weed," "Marijuana Addiction Calendar," and "Marijuana Anonymous." The mean overall quality score of the apps was 3.4 out of 5, indicating poor to acceptable quality. The apps scored the highest on the "functionality" section and the lowest on the "information" section. Of the 4 apps, 3 focused on tracking cannabis use and duration of abstinence, whereas 1 focused on peer support. A limited number of cannabis cessation apps were identified, and those that were available were of low quality due to a lack of evidence-based information. CONCLUSIONS: This study is the first to evaluate the current availability and quality of mobile apps designed for cannabis cessation. Unlike previous research that broadly assessed cannabis-related mobile apps, this study focuses on the limited number of free cannabis cessation tools, reflecting what is most available to the general population. The findings highlight a significant gap between the growing demand for virtual cessation tools and the quality of existing options. With the rising global prevalence of cannabis use disorders, there is an increasing need for robust, accessible, and evidence-based therapeutic options. While mobile health apps may be a viable option to support cannabis cessation, the current landscape is limited by poor quality apps and a lack of evidence-based information. From a real-world perspective, this study highlights the need for users to exercise caution when relying on current cannabis cessation apps and underscores the urgent need for the development and evaluation of new evidence-based digital interventions.
17 May 2026
Read appraisal →African journal of reproductive health
Domestic violence during pregnancy and its maternal and reproductive health consequences in mainland China: A systematic review and meta-analysis
Domestic violence during pregnancy represents a serious public health concern. This study conducted a systematic review of quantitative data to examine its prevalence, factors influencing it, and associated health outcomes in Mainland China. Following PRISMA 2020 guidelines, a meta-analysis of 25 studies was performed using random-effects models, alongside assessments of heterogeneity (I²) and publication bias. The analysis revealed a pooled prevalence of approximately 20%, with psychological violence identified as the most prevalent form. Key contributing factors included socio-economic disadvantage, restricted decisionmaking autonomy, and risky behaviors by partners. Exposure to domestic violence substantially heightened the likelihood of antenatal depression or anxiety, pregnancy complications, and negative birth outcomes such as low birth weight and preterm delivery. Although some heterogeneity was noted, the associations were consistently observed. These findings highlight the significant prevalence and severe health impacts of domestic violence during pregnancy, emphasizing the urgent need for comprehensive maternal healthcare approaches in China. Such strategies should integrate domestic violence screening, mental health services, and initiatives promoting socio-economic empowerment. La violence domestique pendant la grossesse constitue un problème de santé publique grave. Cette étude a réalisé une revue systématique des données quantitatives pour examiner sa prévalence, les facteurs l'influençant et les conséquences sanitaires associées en Chine continentale. Conformément aux directives PRISMA 2020, une méta-analyse de 25 études a été effectuée en utilisant des modèles à effets aléatoires, ainsi que des évaluations de l'hétérogénéité (I²) et du biais de publication. L'analyse a révélé une prévalence globale d'environ 20 %, la violence psychologique étant identifiée comme la forme la plus répandue. Les principaux facteurs contributifs comprenaient les désavantages socio-économiques, une autonomie décisionnelle restreinte et les comportements à risque des partenaires. L'exposition à la violence domestique augmentait considérablement la probabilité de dépression ou d'anxiété anténatales, de complications de la grossesse et d'issues défavorables à la naissance, telles qu'un faible poids de naissance et un accouchement prématuré. Bien qu'une certaine hétérogénéité ait été notée, les associations ont été constamment observées. Ces résultats soulignent la prévalence significative et les impacts sanitaires graves de la violence domestique pendant la grossesse, mettant en évidence le besoin urgent d'approches globales en matière de soins de santé maternelle en Chine. De telles stratégies devraient intégrer le dépistage de la violence domestique, des services de santé mentale et des initiatives favorisant l'autonomisation socio-économique.
16 May 2026
Read appraisal →Archives of women's mental health
Multidimensional psychological and neurophysiological effects of moderate- and low-intensity exercise on pregnant women: an integrative systematic review and Bayesian framework network meta-analysis
BACKGROUND: Pregnancy-related mental health disorders are an increasingly important global public health concern. Exercise interventions have gained attention as safe, non-pharmacological strategies for improving maternal psychological and neurophysiological well-being. However, comparative evidence regarding different exercise modalities and intensities remains limited. OBJECTIVE: This study aimed to systematically evaluate and rank the effects of different prenatal exercise modalities and compare moderate- versus low-intensity exercise protocols on maternal mental health and neurophysiological outcomes during pregnancy. METHODS: A Bayesian network meta-analysis was conducted following PRISMA-NMA guidelines. PubMed, Embase, Cochrane Central, and PsycINFO were systematically searched. Thirty-six randomized controlled trials involving 4,577 pregnant participants were included. Methodological quality was assessed using the Cochrane RoB 2.0 tool. Treatment effects were ranked using the surface under the cumulative ranking curve method. RESULTS: Moderate-intensity aerobic training showed the strongest effect on reducing prenatal depression, with a SUCRA value of 0.89. Yoga-based interventions were particularly effective for anxiety reduction, with a SUCRA value of 0.76, and ranked second in the overall outcome evaluation, with a SUCRA value of 0.82. Neurophysiological findings indicated that yoga showed the greatest benefit for cortisol regulation, whereas aquatic exercise demonstrated distinctive anti-inflammatory effects. Subgroup analyses suggested that moderate-intensity exercise tended to produce greater reductions in depression, while low-intensity exercise showed comparable or superior effects on anxiety and cortisol modulation. Meta-regression further indicated that interventions conducted three times per week and lasting more than 12 weeks were associated with larger effect sizes. The certainty of evidence ranged from moderate to high across primary comparisons. CONCLUSION: This network meta-analysis provides a preliminary evidence hierarchy for prenatal exercise interventions. Moderate-intensity aerobic exercise may be most beneficial for reducing prenatal depression, while yoga appears particularly suitable for anxiety and cortisol regulation. These findings offer clinicians a reference framework for individualized prenatal exercise recommendations and support the integration of exercise into maternal mental healthcare.
16 May 2026
Read appraisal →JMIR mental health
Large Language Models and Their Applications in Mental Health: Scoping Review
BACKGROUND: Large language models (LLMs) are poised to transform mental health care, offering advanced capabilities in diagnosis, prognosis, and decision support. Since their inception, numerous mental health-focused LLMs have emerged in the scientific literature, reflecting the growing interest in leveraging these models across various clinical applications. With a broad range of models available, diverse optimization strategies, and multiple use cases, reviewing the current landscape is critical to understanding where future impact lies. OBJECTIVE: This study aimed to conduct a scoping review investigating the use of LLMs in mental health across diagnostic, prognostic, and decision support tasks. METHODS: We screened 3121 papers from PubMed, Scopus, and Web of Science for studies published between January 2023 and October 2025, using terms related to LLM and mental health. After removing duplicates, 2 reviewers (MCL and WWBG) independently screened the studies, with a third (JJK) to resolve conflicting opinions. We extracted and synthesized information on the models, use cases, datasets, and adaptation methods from selected papers. RESULTS: In total, 41 papers were selected. Many studies included evaluations on OpenAI's GPT series applications: GPT-4 (24 studies, 58.5%) and GPT-3.5 (16 studies, 39%). Others included Bidirectional Encoder Representations from Transformers-derived models (9 studies, 22%), LLaMA (8 studies, 19.5%), and RoBERTa-derived models (6 studies, 14.6%). While all studies initially applied out-of-the-box LLMs, several adapted them through few-shot learning or fine-tuning to better align with specific research goals. The most common use case was in diagnostics (31 studies, 75.6%), while the most common target condition was depression (11 studies, 26.8%). While many studies reported superior performance of LLMs, only a minority of studies (13 studies, 31.7%) validated LLM performance against clinician assessments using real patient data, with the majority relying on proxy outcomes such as clinical vignettes, examination questions, or social media posts. CONCLUSIONS: Despite rapid growth and diversity of LLM applications in mental health, the field remains nascent and exploratory. Future developments must emphasize consistent model adaptation procedures to ensure safety and clinical workflow alignment. Models must also be evaluated on robust evaluation criteria by using standardized protocols and real clinical outcome measures.
16 May 2026
Read appraisal →Journal of affective disorders
Sex differences in cognitive function trajectories and influencing factors in older adults: A machine learning study based on CHARLS and HRS
BACKGROUND: Identify the influencing factors that respectively affect the differences in cognitive function trajectories between China and the United States, and explore the reasons for gender and transnational differences. METHODS: Five waves of data from the China Health and Retirement Longitudinal Study (CHARLS) and the Health and Retirement Study (HRS) were utilized, and Latent Class Growth Models (LCGMs) was employed to identify cognitive trajectories. The optimal machine learning (ML) model was adopted and the Boruta algorithm was used to screen the variables. The SHAP value is used to explain the ranking of feature importance. RESULTS: The XGBoost model performs best among the six ML models. Education was the top influencing factor across groups. Family factors mattered more in China, while U.S. medical and endowment insurance had stronger effects. Men were more affected by external social networks; women were influenced by family responsibilities and community environments. CONCLUSION: Differences were linked to public services, social roles, and traditional culture. These results support the development of tailored interventions targeting gender-specific and culture pathways at the individual, family, society and community levels, offering actionable insights for promoting cognitive health in aging populations globally.
16 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 neurology, neurosurgery, and psychiatry
Comparison of neuropsychological side effects between contemporary radiofrequency ablative neurosurgery for psychiatric disorders and conventional neurosurgical procedures: systematic review and meta-analysis
BACKGROUND: Psychiatric disorders are increasingly contributing to global disability. Despite advances in conservative management, the prevalence of treatment-resistant cases remains high. Meanwhile, neurosurgery for psychiatric disorders (NPD) remains underused, largely due to strict regulations and historical concerns, particularly those related to neuropsychological side effects (NPSE). OBJECTIVE: To address this issue, we conducted a systematic review with meta-analysis to compare NPSE associated with radiofrequency ablative NPD to those observed in neuro-oncological, neurovascular and epilepsy surgeries. METHODS: PubMed, Embase and LILACS databases were searched in April 2024 for articles published in English/Spanish from 1990 to 2022, following Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. RESULTS: A total of 48 articles with 2678 participants were included. The frequency of transient and permanent NPSE in the NPD group ranged from 0.94% to 11.50% and 0.94% to 2.03%, respectively, comparable to the other surgical groups (epilepsy: 0.31-11.70%; vascular: 0.52-22.90%; oncology: 0.94-17.60% for transient NPSE; epilepsy: 0.31-12%; vascular: 0.40-1.96% and oncology: 0.84-1.48% for permanent NPSE). Regarding permanent NPSE, arguably the most critical consideration, the NPD group showed better outcomes in memory, language and social cognition than the epilepsy group, but worse outcomes in executive and perceptual-motor functions. Compared with the vascular group, the NPD group had better executive function but worse complex attention. Finally, the NPD group had fewer permanent deficits than the oncology group in executive function, complex attention and perceptual-motor domains, although language performance was lower. CONCLUSIONS: Contemporary NPD apparently carries a similar risk of NPSE as other conventional neurosurgical procedures, challenging misconceptions and this unjustified barrier to its broader use.
16 May 2026
Read appraisal →Journal of affective disorders
An exploratory randomized controlled trial of an AI-enabled mental health intervention for generalized anxiety.
Generalized Anxiety Disorder (GAD) is prevalent and often co-occurs with depression, contributing to significant disability and healthcare burden. Although treatments such as CBT and SSRIs are effective, access remains limited. This exploratory, randomized controlled trial evaluated the effectiveness of an AI-powered mental health app (PATH) in reducing symptoms of anxiety and depression. A total of 316 UK-based participants (aged 19-70) were randomized to either the intervention group (PATH) or a control group (NHS self-help website). The intervention provided evidence-based strategies including CBT-informed chat therapy and interactive tools. Anxiety (GAD-7) and depression (PHQ-9) scores were measured at baseline, two, eight, and twelve weeks. Of the 316 randomized participants, 235 completed the post-intervention assessment (33.0% attrition in the intervention group vs. 18.5% in the control group). Retention at 8-week and 12-week follow-ups was 77.4% and 54.0%, respectively. At two weeks, the intervention group showed significantly lower GAD-7 and PHQ-9 scores compared to control, with medium effect sizes. At eight weeks, those who continued using the app showed significant reductions in both anxiety and depression, while those who discontinued still showed medium improvements in anxiety. Effects were maintained at twelve weeks, with moderate to large effect sizes. Findings suggest that PATH significantly reduces anxiety and depressive symptoms, particularly with continued use. These results support the app's potential as a scalable, accessible digital intervention to address mental health treatment gaps.
16 May 2026
Read appraisal →Environmental research
Mental health benefits of specific blue space types and characteristics: A systematic evidence map.
Natural environments support mental health and wellbeing, yet limited evidence exists about which types or characteristics are most beneficial. This is particularly true for blue spaces (natural or manmade outdoor environments featuring water). This evidence map investigates the mental health and wellbeing benefits associated with specific blue space types (e.g. coast, river) and characteristics (e.g. sounds). Following PRISMA guidelines, specific keywords were used to search for articles published from inception to March 2025. A total of 139 papers were included, examining the relationships between specific blue space types or characteristics and mental health. Coastal environments were the most frequently studied blue space (94 studies). Affect was the most assessed mental health outcome (74 studies). Most studies were conducted in European and Asian countries. Most studies compared blue spaces to urban or a green space environments, rather than other blue spaces. Only six studies directly compared different blue space types within the same study. Overall, the current evidence base does not allow for comparisons between different blue space types or characteristics, due to the limited number of direct comparisons and substantial heterogeneity across study designs, outcome measures, geographical contexts, blue space descriptions, and study populations. Future research should prioritize direct comparisons between different blue spaces, preferably including the detailed descriptions and analyses of specific characteristics, and examine the role of amount and type of exposure to better inform evidence-based recommendations for blue spaces and mental health.
16 May 2026
Read appraisal →Journal of affective disorders
Sociodemographic and clinical predictors of digital mental health intervention engagement among treatment-seeking psychiatric outpatients
BACKGROUND: Digital mental health interventions (DMHIs) have shown promise improving depression, anxiety, and psychiatric distress, yet real-world engagement remains low. Increasing engagement has great potential to improve the impact of DMHIs, but little is known about the drivers of engagement in naturalistic settings. To better understand predictors of engagement, we examined sociodemographic and clinical characteristics associated with DMHI usage among a large clinical sample of adults. METHOD: 1223 adults (74% White, 68% women, Mage = 36.8 years) with scheduled intake appointments for outpatient psychiatric services were randomized to either a mindfulness-based app (Headspace) or a CBT-based app (SilverCloud). Usage data were automatically collected, and participants were neither required nor compensated to use the apps. RESULTS: Participants engaged with their assigned DMHIs a median of 8 days, with 88.2% of participants using their assigned DMHI at least once. Participants engaged with Headspace for more than twice as many days [IRR (95% CI) = 2.4 (2.1, 2.7)] as SilverCloud. Female sex, white race, a college degree, and older age up to 60 predicted greater engagement. Further, depression severity was associated with engagement in a non-linear manner for those assigned to Headspace, with less engagement at minimal/mild and severe symptoms compared to moderate and moderately-severe symptoms. CONCLUSIONS: These findings indicate meaningful differences in engagement between DMHIs based on sociodemographic and clinical characteristics. There may be opportunities to improve engagement by tailoring DMHI offerings, with a particular emphasis on meeting the needs of less-engaged populations.
16 May 2026
Read appraisal →The British journal of general practice : the journal of the Royal College of General Practitioners
Perinatal mental health training and supervision for primary care and community-based staff: a systematic review
BACKGROUND: Perinatal mental health problems are common yet often under-detected and under-treated in primary care and community services. International guidelines call for integration of mental health into perinatal care, but clarity is lacking on how best to equip non-specialist providers. Training and supervision programmes exist but vary widely in content and delivery, and their effectiveness and feasibility across settings are not comprehensively synthesised. AIM: To evaluate the effectiveness of perinatal mental health training for primary care and community-based providers on provider competence, maternal outcomes, and implementation in routine care. METHOD: Seven databases and grey literature were systematically searched to September 2025. Eligible studies evaluated training delivered to health or community providers involved in perinatal care. Due to heterogeneity, findings were narratively synthesised. Risk of bias was assessed using RoB-2 and ROBINS-I; certainty of evidence appraised using GRADE. RESULTS: Of 707 citations screened, 39 studies were included, spanning high- and low-income countries and involving over 10,000 providers. Interventions varied in intensity and modality, including novel approaches such as extended reality simulation and cascade 'train-the-trainer' models. Training consistently improved provider knowledge, skills, attitudes, and confidence, and was associated with increased screening and referral, particularly when supported by supervision or system-level integration. Patient outcomes were less frequently assessed; where reported, some studies showed reductions in depressive symptoms and improvements in broader maternal outcomes, though findings were mixed. CONCLUSION: Training enhances provider capacity in perinatal mental health, but evidence on patient impact, sustainability, cost-effectiveness, and equity remains limited, highlighting priorities for future research and policy.
16 May 2026
Read appraisal →JMIR mental health
Adoption of Digital Mental Health Interventions in National Health Service England, Scotland, and Wales: Freedom of Information Questionnaire Study
BACKGROUND: Digital mental health interventions (DMHIs) have been widely promoted to improve access to mental health care within the UK National Health Service (NHS), particularly following the COVID-19 pandemic. In 2015, a total of 48 technologies were reportedly used in NHS services in England, but over the past decade, substantial changes to regulatory requirements, evidence standards, and procurement processes have reshaped the digital mental health landscape. There is limited clarity regarding which DMHIs are currently being formally procured and funded by NHS mental health services across the United Kingdom. OBJECTIVE: This study aimed to identify and describe the DMHIs currently procured, contracted, or paid for by NHS mental health service providers in England, Scotland, and Wales for adult common mental health problems and to compare current procurement practices with findings reported in 2015. METHODS: Freedom of Information requests were submitted to all NHS mental health trusts in England and all health boards in Scotland and Wales. Responses were collated and screened to provide an updated and extended record of which technologies are reportedly procured or paid for by services. RESULTS: In total, 19 different DMHIs were identified as being procured across mental health service providers for adult common mental health problems at the time of data collection. This demonstrates a substantial reduction in the number of technologies being adopted into practice compared to the 48 reported in England in 2015. The findings reveal several key insights, including that only 2 technologies have remained in use for a decade, and they shed light on the types of technologies being selected and the variations in procurement practices among the 3 national health services. CONCLUSIONS: Despite the expansion of the digital mental health marketplace, the number of DMHIs formally procured by NHS mental health services has markedly decreased over the past decade. This consolidation may reflect increased selectivity and the adoption of higher-quality products, driven by strengthened regulatory oversight, evidence standards, and national guidance. Although these developments may enhance safety and quality assurance, they also raise important questions about innovation, market sustainability, and equitable access to digital mental health care. Ongoing monitoring of procurement practices is needed to inform policy, service design, and the future development of DMHIs.
16 May 2026
Read appraisal →Journal of medical Internet research
Understanding Patient-Reported Offenses in Electronic Health Records: Cross-Sectional Mixed Methods Survey
BACKGROUND: Patients' access to their electronic health record (EHR) supports their participation and satisfaction with care. Despite the benefits, some patients have been upset after reading their EHR. Additionally, health care professionals are concerned that patients, particularly those with mental health conditions, may be offended, and they have expressed a need for further guidelines on how to write EHRs. Experiences among various patient groups are essential to support the relationship between patients and professionals. However, prior studies have often focused on single patient groups or specific clinical contexts, leaving a limited understanding of differences across multiple patient groups. OBJECTIVE: This study aimed to determine whether certain patient groups are more likely to feel offended while reading their EHRs and which information is perceived as offensive and to provide a comparison across multiple patient groups using a mixed methods approach. METHODS: A cross-sectional survey was conducted via the Finnish national patient portal using a web-based patient survey, adopting a mixed methods approach. The survey included multiple-choice and open-ended questions. The total sample comprised 4681 respondents. The survey respondents were placed into 4 patient groups: those who had received care for mental health, cancer, or other conditions and those who had received no care. Associations between the type of care and patients who felt offended were estimated using multivariate binary logistic regression. Inductive content analysis (n=502) was conducted to identify information perceived as offensive in the EHR. RESULTS: The patients who had received mental health care (166/654, 25.4%) or cancer and mental health care (9/39, 23.1%) were more likely to be offended by information in their EHR compared to the other groups (cancer care: 37/375, 9.9%; other conditions care: 383/3316, 11.6%; no care: 22/206, 10.7%; other conditions care: odds ratio 0.37, 95% CI 0.29-0.46; P<.001; model A). Additionally, female patients, those with bad or very bad health conditions, and patients with bachelor's or master's degrees were significantly more likely to feel offended. Errors, the health care professionals' disrespectful language, and perceived unnecessary information were the most frequently mentioned reasons for being offended. Patients with mental health care reported more often that unnecessary information and professionals' opinions and word choices were experienced as offensive compared to other patients. CONCLUSIONS: This study contributes new knowledge by identifying differences across multiple patient groups. Although a minority of patients felt offended by their EHR, health care professionals should consider that some patients, particularly those who have received mental health care or cancer and mental health care, may be offended by specific information or word choices in their EHRs. To address this, health care professionals should receive education on how to write their notes in a neutral tone and avoid potentially offensive topics. Improving the quality of EHRs could strengthen the relationship between patients and professionals.
15 May 2026
Read appraisal →Journal of neurology
The effects of computerized cognitive training on cognitive function in patients with mild cognitive impairment: a systematic review and meta-analysis
BACKGROUND: We sought to quantify the effects of computerized cognitive training (CCT) on patients with mild cognitive impairment (MCI), to inform the development of clinical non-pharmacological intervention strategies. METHODS: We searched PubMed, Cochrane Library, Embase, and Web of Science databases from 2007 to February 2026 for randomized controlled trials (RCTs) of CCT in patients with MCI. Outcomes including global cognition, specific cognitive domains, psychosocial functioning, and activities of daily living were analyzed separately by stata 18 and Review Manager 5.4. RESULTS: We included 30 studies and 2056 participants. Our meta-analysis revealed that, compared with control conditions, CCT led to a significant moderate improvement in overall cognitive function, standardized mean difference (SMD) = 0.62, 95% confidence interval (CI) = 0.43-0.81; I2 = 73%. Subgroup analysis showed that intervention characteristics-including session duration, training device, total intervention length, weekly frequency, sample size, and assessment tools-yielded similar improvements in global cognition, with no statistically significant differences between subgroups. These findings indicate that the cognitive benefits of CCT are consistent and robust. CONCLUSION: This systematic review and meta-analysis shows CCT significantly improves global cognitive function in MCI patients, with stable benefits across subgroups. Despite clear overall effects, high heterogeneity, varying training protocols and assessment tools exist. Further large, long-term RCTs are needed to clarify CCT's long-term effects and support its standardized clinical application. TRIAL REGISTRATION: PROSPERO International prospective register of systematic reviews CRD420261328762; https://www.crd.york.ac.uk/PROSPERO/view/CRD420261328762 .
15 May 2026
Read appraisal →Journal of affective disorders
Machine learning and SHAP-based interpretation of dietary antioxidants' impact on comorbid MDD and suicidal ideation.
OBJECTIVE: To develop and validate a machine learning model for predicting major depressive disorder (MDD) with suicidal ideation (SI) by incorporating dietary antioxidants, and to elucidate the specific contribution of these antioxidants in the prediction. METHODS: Data were obtained from NHANES 2007-2010. Dietary antioxidants, including vitamins, minerals, and polyphenols, were the primary predictors. Demographic, lifestyle, and health-related variables were also included. Collinear variables were removed, class imbalance was corrected, and data were normalized prior to modeling. Twelve machine learning algorithms were compared using a systematic benchmarking protocol within the sklearn framework: Random Forest (RF), LightGBM, AdaBoost, XGBoost, Extra Trees, CatBoost, Gradient Boosting Decision Trees, Support Vector Machine, Decision Tree, Gaussian Naïve Bayes, Stochastic Gradient Descent, and K-Nearest Neighbors. SHapley Additive exPlanation (SHAP) values were calculated to interpret feature contributions within the best-performing model. RESULTS: A total of 9306 participants were included, of whom 247 with MDD comorbid with SI. After preprocessing, 31 dietary antioxidants and 9 demographic and health-related variables were retained for modeling. The RF classifier achieved the highest performance with an accuracy of 83%, precision of 0.825, recall of 0.838, area under the ROC curve of 0.927, and F1 score of 0.831. SHAP analysis identified vitamin C, kaempferol, myricetin, peonidin, luteolin, eriodictyol, hesperetin, pelargonidin, and zinc as the most influential predictors. CONCLUSION: The RF model exhibited superior predictive capability for comorbid MDD and SI. SHAP analysis highlighted the critical roles of dietary antioxidants, particularly vitamin C and kaempferol, in predicting these outcomes.
15 May 2026
Read appraisal →Journal of affective disorders
Systematic review and meta-analysis of intranasal esketamine for treatment-resistant depression: Evidence from real-world studies
BACKGROUND: Intranasal esketamine has demonstrated efficacy and safety for treatment-resistant depression in randomized controlled trials, however the generalizability of these findings to clinical practice remains unclear. This systematic review and meta-analysis aimed to synthesize real-world evidence of intranasal esketamine in patients with treatment-resistant depression. METHODS: A systematic search was conducted in PubMed, Embase, and PsycINFO up to April 2025 to identify observational studies reporting clinical outcomes of intranasal esketamine in adults with treatment-resistant depression. Standardized mean changes in depressive symptoms and odds ratios for remission were calculated. Random-effects meta-analyses were performed, followed by meta-regression to explore potential moderators. The study protocol was prospectively registered in PROSPERO (CRD420251025075). RESULTS: Nine studies were included in the final synthesis. The analyses demonstrated that intranasal esketamine showed substantial effectiveness, with an effect size of Hedges' g = -1.98 (95% CI -2.94 to -1.03; p < 0.0001). Patients were about five times more likely to achieve remission at three months compared with the induction phase (OR = 5.1, 95% CI 2.29-11.39). Treatment effectiveness was not influenced by factors such as gender or the presence of comorbidities, such as anxiety, personality, or substance use disorders. The pooled prevalence of any adverse event, based on studies that reported safety data, was 82% (95% CI 69-92). The most frequent was dissociation, reported in 49% of patients (95% CI 37-62). CONCLUSIONS: This study provides consistent real-world evidence supporting that Intranasal esketamine was associated with meaningful symptom reduction and rising remission rates over time in real-world settings. Given the observational nature of the evidence and the absence of control groups, effect sizes should be interpreted with caution. Nonetheless, these findings provide valuable insight into esketamine's effectiveness and tolerability in routine practice and highlight the need for controlled comparative studies to guide long-term treatment strategies.
15 May 2026
Read appraisal →Computers in biology and medicine
Dual-model deep learning for Alzheimer's prognostication
Disease-modifying therapies for Alzheimer's disease demand precise timing decisions, yet current predictive models require longitudinal clinical observations and provide no uncertainty quantification-rendering them impractical at the critical first-visit encounter when treatment decisions must be made. We developed PROGRESS (PRognostic Generalization from REsting Static Signatures), a dual-model deep learning framework that transforms a single baseline cerebrospinal fluid (CSF) biomarker assessment into actionable prognostic estimates without requiring prior clinical history. The framework addresses two complementary clinical questions: a probabilistic trajectory network predicts individualized cognitive decline parameters with calibrated uncertainty bounds that achieve near-nominal coverage, enabling honest prognostic communication rather than false precision; and a deep survival model estimates time-to-conversion from mild cognitive impairment to dementia. Using data from over 3000 participants across 43 Alzheimer's Disease Research Centers in the National Alzheimer's Coordinating Center database, PROGRESS substantially outperforms existing approaches including Cox proportional hazards, Random Survival Forests, and gradient boosting methods for survival prediction. Risk stratification identifies patient groups with seven-fold differences in conversion rates, enabling clinically meaningful treatment prioritization. Leave-one-center-out validation demonstrates robust generalizability, with survival discrimination remaining strong across all held-out clinical sites despite heterogeneous measurement conditions spanning four decades of assay technologies. By combining superior survival prediction with trustworthy trajectory uncertainty quantification, PROGRESS bridges the gap between biomarker measurement and personalized clinical decision-making-providing the prognostic timeline that current staging approaches cannot offer.
15 May 2026
Read appraisal →Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Empowerment gain and causal model construction: children and adults are sensitive to controllability and variability in their causal interventions
Learning about the causal structure of the world is a fundamental problem for human cognition. Causal models and especially causal learning have proved to be difficult for large pretrained models using standard techniques of deep learning. In contrast, cognitive scientists have applied advances in our formal understanding of causation in computer science, particularly within the causal Bayes net formalism, to understand human causal learning. In the very different tradition of reinforcement learning (RL), researchers have described an intrinsic reward signal called 'empowerment' which maximizes mutual information between actions and their outcomes. Empowerment may be an important bridge between classical Bayesian causal learning and RL and may help to characterize causal learning in humans and enable it in machines. If an agent learns an accurate causal world model, they will necessarily increase their empowerment, and increasing empowerment will lead to a more accurate causal world model. Empowerment may also explain distinctive features of children's causal learning, as well as providing a more tractable computational account of how that learning is possible. In an empirical study, we systematically test how children and adults use cues to empowerment to infer causal relations and design effective causal interventions. This article is part of the theme issue 'World models in natural and artificial intelligence'.
15 May 2026
Read appraisal →Neurology. Clinical practice
Clinical Meaningfulness of Donanemab in Early Symptomatic Alzheimer Disease: Data From the Randomized Phase 3 TRAILBLAZER-ALZ 2 Trial
BACKGROUND AND OBJECTIVES: Understanding the meaningfulness of clinical trial outcomes is essential for people living with Alzheimer disease (AD) and their clinicians to make evidence-based shared treatment decisions in real-world clinical care. Donanemab, a monoclonal antibody targeting the insoluble form of β-amyloid found in plaques, significantly slows cognitive and functional decline of AD in participants with mild cognitive impairment (MCI) or mild AD-related dementia. This analysis reviews the efficacy of donanemab across various clinical outcome assessments, using both published data and new complementary analyses to provide context on its potential benefits for patients and caregivers. METHODS: We present findings from prespecified and post hoc analyses from the TRAILBLAZER-ALZ 2 trial. Clinical outcomes assessed were Integrated AD Rating Scale (iADRS), comprising the 13-item AD Assessment Scale-Cognitive Subscale (ADAS-Cog13) and AD Cooperative Study-Instrumental Activities of Daily Living (ADCS-iADL); Clinical Dementia Rating (CDR)-Sum of Boxes (CDR-SB) for clinical severity and individual cognitive and functional domains; CDR-Global for clinical severity stage progression; meaningful within-patient change (MWPC); and ADCS-Activities of Daily Living dependence score. RESULTS: Donanemab reduced the risk of progression from MCI to mild AD by 33% (hazard ratio [HR] = 0.67; 95% CI 0.52-0.87; p = 0.003) and from mild to moderate AD by 50% (HR = 0.50; 95% CI 0.33-0.78; p = 0.002). In addition, donanemab reduced MWPC risk over 76 weeks by 38% for CDR-SB (HR = 0.62; 95% CI 0.52-0.75; p < 0.001) and 30% for iADRS (HR = 0.70; 95% CI 0.58-0.84; p < 0.001). Donanemab-treated participants exhibited significant slowing of clinical progression across multiple ADAS-Cog13 and ADCS-iADL items and all CDR-SB cognitive and functional domains. Donanemab also slowed progression of dependence least-squares mean change difference, -0.14 [95% CI -0.24 to -0.04; p = 0.007]), representing 23% slowing of progression (95% CI 6.17%-40.32%), and reduced risk of progression to requiring in-home support by 27% (HR = 0.74; 95% CI 0.59-0.91; p = 0.005). DISCUSSION: These results add to the evidence and further support clinically meaningful donanemab-mediated effects on cognition and function for patients and their caregivers and may aid communication of realistic treatment expectations and informed decision-making. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov NCT04437511. Submitted: June 17, 2020; First patient enrolled: June 19, 2020. clinicaltrials.gov/study/NCT04437511 EudraCT Number 2020-000077-25. Start date of recruitment: June 19, 2020. clinicaltrialsregister.eu/ctr-search/trial/2020-000077-25/results.
15 May 2026
Read appraisal →Scientific reports
Exploring the psychological and physiological effects of a virtual reality-based bicycle exercise in hemodialysis patients: a randomized controlled trial
This study aimed to evaluate the efficacy of a virtual reality (VR)-based bicycle exercise in psychological distress (depression and anxiety) and selected biochemical parameters among patients receiving maintenance hemodialysis (MHD). A total of 70 patients from the dialysis station at Changzhou Medical District of the No. 904th Hospital were randomly allocated into study group(VR-based bicycle exercise, n = 35) and control group(routine nursing care, n = 35). The depression and anxiety levels were assessed by Self-rating Depression Scale (SDS), and Self-rating Anxiety Scale (SAS) respectively. Physiological indicators, including serum creatinine (Scr) and blood urea nitrogen (BUN) levels, were also analyzed. Following the intervention, the study group exhibited significant reductions in SDS and SAS scores (both P < 0.05). The levels of Scr and BUN in peripheral blood were also observed to be significantly decreased in study group (P < 0.05). Correlation analysis revealed a significant positive correlation between SAS/SDS scores with both Scr and BUN levels (P < 0.05 or P < 0.01). Furthermore, regression models identified SAS scores as significant predictors of Scr and BUN levels, accounting for 27.8% and 31.9% of their variance, respectively (P < 0.05 or P < 0.01). VR-based bicycle exercise can improve psychological well-being, and was associated with beneficial changes in pre-dialysis serum biomarkers (Scr and BUN) in MHD patients. This integrated intervention represents a promising non-pharmacological strategy to complement standard care in MHD patients.
15 May 2026
Read appraisal →Translational psychiatry
Acute placebo responsiveness predicts longitudinal expectation effects in antidepressant treatment
Positive treatment expectations are known to enhance antidepressant efficacy. Experimental placebo studies aim to provide mechanistic insights into this association, yet their findings have not been clearly linked to clinical expectation effects within individual patients. In a randomized crossover trial, 61 participants with major depressive disorder (31 women, 30 men) completed an emotion classification task and mood assessments after administration of a saline nasal spray labeled as either oxytocin (sham oxytocin treatment, deceptive placebo) or saline (control). Forty-five participants subsequently underwent antidepressant treatment and were monitored weekly for treatment expectations and depressive symptoms, with follow-ups extending to three months. Using linear mixed-effect models, we examined trajectories of clinical expectation effects and their prediction by experimental placebo-induced changes in emotional processing. Sham oxytocin treatment acutely induced positive expectations, improved mood, and shifted emotional processing toward positivity. Longitudinal modeling revealed that weekly reductions in depressive symptoms were predicted by higher treatment expectations reported the preceding week. These clinical expectation effects were most pronounced in individuals exhibiting strong acute placebo responses in emotional processing. Overall, our findings indicate that positive treatment expectations facilitate critical aspects of emotional processing in depression and that individual responsiveness generalizes across different treatments and time windows. Experimental placebo assessments offer an ecologically valid model for elucidating mechanisms and predictors of clinical expectation effects and highlight expectation sensitivity as a promising target for treatment optimization.
15 May 2026
Read appraisal →BMJ mental health
Emulated trial of artificial intelligence use and subsequent depressive outcomes in a survey of US adults
BACKGROUND: Generative artificial intelligence (AI) use has been suggested to have adverse mental health consequences but a causal relationship has not been examined. OBJECTIVE: To simulate a randomised controlled trial of AI use in a work, school or personal context by applying target trial emulation to multiple waves of data from a nationally representative survey. METHODS: We conducted a target trial emulation using non-probability survey data from three waves of a nationally representative survey conducted between 18 June 2024 and 8 January 2025. Participants aged ≥18 years reported generative AI use frequency at baseline. High-frequency use was defined as multiple times per week or more. The primary outcome was depressive symptom severity measured using the Patient Health Questionnaire 9-item (PHQ-9) at follow-up. Generalised causal forests assessed heterogeneity of treatment effects. FINDINGS: Among 19 099 participants assessed at baseline, 2862 (15.0%) reported AI use at least multiple times per week. A subset of 3109 (16.3%) returned for follow-up. In the primary weighted analysis, high-frequency use was not significantly associated with change in PHQ-9 score at follow-up (mean difference -0.18, 95% CI -0.94 to 0.59; p=0.65). Multiple sensitivity analyses using alternate outcome definitions also did not identify significant causal effects. Generalised causal forests yielded no significant evidence of heterogeneity of effect (p=0.81). CONCLUSIONS: In an emulated randomised trial among US adults, generative AI use was not associated with subsequent depressive symptoms. This result does not support the premise that AI use causes greater depressive symptoms, although adverse outcomes among vulnerable individuals cannot be excluded. CLINICAL IMPLICATIONS: AI use is unlikely to cause increased depressive symptoms among most US adults. Continued monitoring should clarify potential risks among vulnerable populations.
15 May 2026
Read appraisal →BMJ open
Prevalence of anxiety and depression in adults who are high users of healthcare services and magnitude of associated costs: a systematic review
OBJECTIVES: Anxiety and depression are common and associated with higher use of general healthcare services. The aims of this systematic review were to (1) estimate the prevalence of anxiety and depression in adults who are high or costly users of general healthcare services in comparison to routine users and (2) estimate the magnitude of healthcare costs associated with the presence of anxiety and depression. DESIGN: Systematic review of the available literature. DATA SOURCES: MEDLINE, PsycINFO, EMBASE, CINAHL, PROSPERO and Cochrane Library were systematically searched without language restriction from inception to 1 April 2019 and updated on 25 October 2022, 16 October 2024 and 18 February 2026. ELIGIBILITY CRITERIA: Eligible studies described adults aged ≥18 years who were defined as high or costly general healthcare users and where the prevalence and/or associated costs of anxiety and/or depression were quantified. DATA EXTRACTION AND SYNTHESIS: Three reviewers independently extracted information on study characteristics, exposure and outcomes. RESULTS: From the 38 412 identified articles, 27 studies from 10 countries (in Europe, North America and Asia) involving 6 145 907 participants met eligibility criteria and were included. There were wide variations in the estimated prevalence of anxiety (3.8-67.2%) and depression (4.7-77.9%) among high healthcare users. The prevalence of both disorders was higher among high healthcare users than routine users in all studies with non-high user comparator groups. Only four studies investigated healthcare costs associated with depression. These uniformly reported that general healthcare costs are higher for those with depression than those without. No studies investigated costs associated with anxiety. CONCLUSIONS: Anxiety and depression are over-represented among high or costly healthcare users, although accurate quantification of the magnitude of difference is precluded by significant methodological heterogeneity and variability in definitions used. Improved identification of covert mental health problems is essential for the provision of effective interventions for patients and healthcare expenditure reduction. Future research should prioritise a standardised approach, with agreed definitions for high and/or costly healthcare use in different contexts. PROSPERO REGISTRATION NUMBER: CRD42018102628.
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 →Neuroscience
Recent trends in electrodermal activity signal processing and deep learning methods for emotion recognition
Electrodermal activity (EDA) has emerged as one of the most informative physiological signals for emotion recognition due to its close association with sympathetic nervous system activation. Although research on affective computing has expanded rapidly in recent years, systematic investigations of signal-processing techniques specifically tailored to EDA remain limited. This gap is significant because the selection of decomposition and signal processing strategies directly influences the accuracy, robustness, and interpretability of emotion recognition systems. Furthermore, defining emotion itself and tracing its physiological manifestations through EDA, from raw measurements to recognized affective states, remains an open challenge that highlights the need for methodological rigour. In this review, we synthesise developments published between 2018 and 2025, with a dual focus on advanced EDA signal processing methods and deep learning (DL) approaches for emotion recognition. We review common EDA acquisition sites, discuss challenges arising from non-stationarity and inter-subject variability. The review systematically compares time-domain, frequency-domain, time-frequency, and advanced time-series analysis techniques, alongside emerging end-to-end DL architectures for affective modelling. Unlike prior surveys that emphasise system-level design, this work adopts a signal-processing-centric and physiology-informed perspective. Additionally, we provide a structured comparative evaluation of widely used EDA decomposition methods using key performance metrics, offering a unified framework for method selection and interpretation. Overall, this review serves as a comprehensive resource for researchers and practitioners while advocating hybrid approaches that integrate the interpretability of classical signal processing with the predictive power of predictive methods.
15 May 2026
Read appraisal →PloS one
The impact of walking on creative thinking: A systematic review and meta-analysis
BACKGROUND: Walking is associated with many benefits, from improved mental health to a reduced risk of mortality - but can it boost creative thinking? Current evidence suggests a positive effect of physical activity on creative thinking, but the specific effect of walking has not been adequately explored. METHODS: We conducted a systematic search of the PsycINFO, MEDLINE, Scopus, and ProQuest databases from inception to 8 January 2025 using search terms related to walking and creativity. We then performed meta-analyses of Cohen's d effect sizes to assess the effect of walking on creative thinking, as represented by divergent thinking (the ability to generate novel and useful ideas) and convergent thinking (the ability to analyze and select ideas to find the best solution). We assessed study quality using a creativity-specific tool, and the certainty of the evidence with the Grading of Recommendations Assessment, Development and Evaluation Framework. RESULTS: We identified 23 studies (12 randomized experimental studies, nine non-randomized experimental studies, two observational studies) from 16 articles including a total of 1,036 participants, most of whom were post-secondary students. We found moderate certainty evidence of a large effect of walking on divergent thinking (d = 0.93 [95% CI 0.44, 1.42]), and very uncertain evidence of a null effect of walking on convergent thinking (d = 0.16 [95% CI -0.31, 0.63]). Sensitivity analyses of randomized trials only found similarly large effects of walking on divergent thinking ability (d = 0.82 [95% CI 0.35, 1.28]). CONCLUSION: Results suggest that walking likely results in a large increase in divergent thinking, indicating its potential as an intervention to stimulate creative idea generation.
15 May 2026
Read appraisal →JMIR mental health
Machine Learning for Comparative Antidepressant Selection in Major Depressive Disorder: Systematic Review
BACKGROUND: Major depressive disorder (MDD) affects approximately 1 in 6 adults during their lifetime, yet antidepressant selection relies predominantly on trial-and-error, with response rates of only 42% to 53%. While machine learning (ML) models have shown promise in predicting treatment outcomes, most focus on single treatments rather than comparative selection across therapeutic alternatives, limiting their clinical utility for the medication choice decisions that clinicians face in practice. OBJECTIVE: This systematic review evaluates ML approaches that examine 2 or more pharmacological interventions for predicting treatment outcomes in MDD, with a focus on their capacity to facilitate comparative treatment selection between medications or medication classes for individual patients. METHODS: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we searched PubMed, Scopus, and Web of Science for studies published from 2015 to 2025. We included studies involving adults with MDD that used ML models to predict treatment outcomes across 2 or more pharmacological treatments and reported medication-specific prediction outcomes. Risk of bias was assessed using PROBAST-AI (Prediction Model Risk of Bias Assessment Tool for Artificial Intelligence). We conducted a narrative synthesis organized by modeling strategies, data integration approaches, validation methodologies, and performance patterns. RESULTS: From 5370 initial records, 19 studies met the inclusion criteria, with dataset sample sizes ranging from 49 to 77,226 participants. Studies employed 3 distinct modeling strategies: drug-specific supervised models trained independently for each medication, subtype- or trajectory-based approaches using clustering methods to identify differential response patterns, and a unified differential prediction framework generating calibrated cross-treatment predictions. Performance varied substantially, with area under the curve values ranging from 0.59 to 0.95 and classification accuracies between 62% and 95.4%, though high performance was concentrated in studies with small samples, high-dimensional neurobiological features, and internal-only validation. Only 7 studies conducted external validation, which generally yielded more conservative performance estimates. Feature informativeness was more consistently associated with performance variation than algorithm complexity. Most studies did not formally distinguish between prognostic features predicting general outcomes and predictive features identifying differential medication responses, and none applied formal explainability techniques. CONCLUSIONS: ML for comparative antidepressant selection remains in an early stage of development. Only 1 study implemented a unified framework directly supporting patient-level treatment ranking. Key barriers to clinical translation include insufficient distinction between prognostic and predictive markers, limited cross-trial validation, near-absent calibration reporting, and absent explainability. Future research should prioritize unified comparative frameworks with calibrated predictions, rigorous external validation on diverse cohorts, explicit modeling of heterogeneous treatment effects, and integration of explainability into model development.
14 May 2026
Read appraisal →European journal of psychotraumatology
Attenuating trauma- and cocaine-related intrusions by blocking memory reconsolidation with minocycline: protocol for a transdiagnostic randomized controlled trial
Background: Post-traumatic stress disorder (PTSD) and substance use disorders frequently co-occur, with their comorbidity linked to poorer treatment outcomes. Beyond this epidemiological overlap, PTSD and, in particular, cocaine use disorder (CUD) have also been conceptually linked, as maladaptive memory processes are thought to play a central role in both of their etiopathogeneses. PTSD and CUD are each characterized by intrusive memories - either trauma or cocaine related - that sustain distress and craving, respectively. These parallels have motivated transdiagnostic mechanistic research targeting memory reconsolidation. Matrix metalloproteinase-9 (MMP-9) is critically involved in synaptic plasticity and memory updating, and has been suggested as a pharmacological entry point for modulating reconsolidation-related processes.Objective: This study examines whether pharmacological interference with reconsolidation via MMP-9 inhibition by minocycline modulates trauma- and cocaine-related intrusive memories and their neurobiological correlates in PTSD and CUD.Method: In this monocentric, randomized, double-blind, placebo-controlled trial (NCT05902819), individuals with PTSD (n = 60) or CUD (n = 60) receive a single dose of minocycline - a tetracycline inhibiting MMP-9 - or placebo prior to an individualized, imagery-based reactivation of their most salient intrusive memory. Intrusive memories in day-to-day life are assessed using event-based ecological momentary assessment and pre-post intrusion questionnaires. Neurobiological correlates of maladaptive memory processing are indexed through functional magnetic resonance imaging and magnetic resonance spectroscopy, and peripheral biomarkers are obtained from blood, urine, and hair samples. Diagnostic status and comorbid symptoms are characterized using clinical interviews and standardized self-report measures.Conclusions: This trial adopts a mechanistic framework to investigate whether minocycline-augmented memory reactivation modulates intrusions and associated neurobiological markers in PTSD and CUD. By directly targeting candidate memory mechanisms shared by both disorders, it evaluates a transdiagnostic reconsolidation-based approach and is expected to inform models of intrusive memory persistence and updating. The findings may guide future development of memory-focused interventions in trauma- and addiction-related disorders. This randomized controlled trial evaluates a transdiagnostic, mechanism-focused intervention targeting maladaptive memory processes in PTSD and cocaine use disorder.Minocycline is used as an MMP-9 inhibitor to modulate intrusive trauma- and cocaine-related memories following brief imagery-based memory reactivation.The study integrates multimodal neurobiological assessments (fMRI, MRS, peripheral biomarkers) with ecological momentary intrusion measures to characterize changes in memory-related mechanisms.
14 May 2026
Read appraisal →PloS one
Narrative-affect discrepancy as a regulated degree of freedom in 351,734 relationship narratives
In naturalistic emotional narratives, the intensity of expressed affect does not scale proportionally with narrative structure. Using 351,734 English-language relationship narratives from online support communities (the ANEST Narrative-Affect Dataset, ANAD v1.1.0), we constructed a two-dimensional expressive space defined by narrative complexity (N) and linguistically inferred affective intensity (A), with their signed discrepancy ([Formula: see text]) treated as a derived coordinate. Rather than converging toward low discrepancy, human narratives occupied a broad but structured space consistent with trade-offs between relational exposure and cognitive effort. We identified four empirically separable regimes of expressive organization: coupled expression (non-extreme discrepancy; the complement of the extreme regimes), strategic understatement (high A, low N, D < 0), strategic overstatement (high N, low A, D > 0), and collapse (high A with limited narrative scaffolding). A data-anchored cost model (NCS) formalized these regimes as arising from the interaction of exposure risk and cognitive effort, not from discrepancy minimization per se. Coupled expression dominated the corpus (91.3%), while the remaining regimes formed smaller but non-negligible subpopulations (Understatement: n = 20,223; Collapse: n = 8,040; Overstatement: n = 2,223), indicating that extreme discrepancy configurations occur systematically rather than as isolated outliers. As a comparative probe, we projected an RLHF-aligned large language model into the same space using matched prompts and identical feature extraction. Because D is a deterministic function of N and A, expressive extent was quantified as convex hull area in the clipped [Formula: see text] plane. Under this procedure, the model occupied a markedly smaller region (approximately [Formula: see text] smaller hull area; bootstrap 95% CI [1.68, 1.70]; permutation p < 0.0001) and concentrated near low-discrepancy configurations, with sparse occupancy of extreme under- and overstatement regimes. Together, these findings suggest that narrative-affect discrepancy is a measurable and regulated dimension of emotional expression and provide a reproducible geometric basis for comparing expressive degrees of freedom across populations and systems.
14 May 2026
Read appraisal →Sleep
Effects of a guided digital intervention on sleep and mental health outcomes in university students - a randomized controlled trial
STUDY OBJECTIVES: To evaluate the effectiveness of a guided digital intervention targeting sleep and circadian rhythms in university students. METHODS: We randomized 195 university students with self-reported insomnia (Insomnia Severity Index ≥10) from nine Dutch Universities to receive either the 5-week guided digital cognitive-behavioral therapy for insomnia "i-Sleep & BioClock", or digital sleep psychoeducation and sleep monitoring. Online assessments were at baseline, 3 (mid-treatment), 6 (post-treatment), and 18 (follow-up) weeks. The primary outcome was insomnia severity at 6 weeks. Secondary outcomes were symptoms of depression and anxiety; functioning; quality of life (QoL); academic performance; and sleep and light exposure diary outcomes. RESULTS: From Nov 1st 2023 until Sept 5th 2024, we randomly assigned 96 students (age 23.8 ± 3.8, 75.4% female) to the intervention and 99 to the control condition, stratified by university and gender. Fifty-one percent completed the post-treatment assessment, and 30% the follow-up. 31% were intervention completers. Linear mixed models showed no significant time x treatment interaction for insomnia severity ("i-Sleep & BioClock": baseline ISI M = 16.8, SD = 4.0; post-treatment ISI M = 10.2, SD = 3.9; Digital psychoeducation: baseline ISI M = 16.5, SD = 3.6; post-treatment ISI M = 11.1, SD = 4.2). The difference in change between groups was -1.17 points (95% CI -2.72 to 0.38), p=.14, Cohen's d = -0.31. Most secondary outcomes showed no significant time x treatment effects. CONCLUSIONS: "i-Sleep & BioClock" was not more effective than online psychoeducation on most outcomes, possibly due to active elements present in both conditions or low adherence. Future research should identify factors for early disengagement and investigate which intervention components drive improvements in sleep. CLINICAL TRIAL: Effectiveness of a Guided Self-help Intervention for Improving Sleep in University Students, https://clinicaltrials.gov/study/NCT06023693, prospectively registered at ClinicalTrials.gov on August 3rd, 2023.
14 May 2026
Read appraisal →Sleep
Testing affect regulation theories of dreaming
STUDY OBJECTIVES: Dreaming is theorized to regulate waking affect via fear extinction learning, similar to exposure therapy. We tested whether fear in dreams was associated with lower negative affect (NA) and higher positive affect (PA) on subsequent mornings and whether these effects were moderated by emotion regulation strategies or mixed dream emotion states. Additionally, we examined whether individuals with higher average dream fear showed more adaptive emotion regulation. METHODS: We employed a natural language processing tool and Bayesian multilevel modeling using online daily survey data in a community sample (n = 536; 85.6% women; Mage = 39.3 ± 17.3; days = 4715). RESULTS: Frightening dreams were associated with more negative affect the following morning (+7% NA, pd = 100%), which was amplified for individuals with more adaptive emotion regulation (+3% NA, pd = 98.69). Dreams concurrently high in fear and joy were related to 20% higher odds of zero NA the next morning (pd = 97.82%). Higher average dream joy was related to 9% higher morning PA (pd = 98.79%). Individuals with higher average dream fear showed greater adaptive emotion regulation (β = 0.18, pd = 99.93%). CONCLUSIONS: Findings supported affect continuity from dream to wake, yet affect regulation at the individual level. Frightening dreams were related to worse morning affect, with a more pronounced effect among high-regulation individuals. High-regulation individuals tended to experience more fearful dreams. Mixed dream emotions were related to mornings without NA, and dream joy was related to higher morning PA. Our findings suggest adaptive, though dynamic, effects of dream emotions on psychological functioning.
14 May 2026
Read appraisal →Biomedical physics & engineering express
Differentiating depression subtypes through three-electrode EEG and emotional stimuli: a machine learning approach
We propose an EEG-based framework for depression subtype assessment using emotion-modulated neural dynamics elicited by immersive virtual reality (VR). EEG was recorded from 70 participants (31 first depressive episode, FDE; 18 recurrent depressive episode, RDE; 21 control participants, HC) using a compact frontal montage (Fp1/Fpz/Fp2) during positive and negative VR conditions, focusing on the immediate post-stimulus regulation period. Subject-level features were analyzed with a linear mixed-effects model to probe group-by-condition interaction patterns, and a Bi-Emotional Siamese Network (BESN) was developed to model baseline-anchored within-subject deviations by fusing positive/negative reactivity streams with path-signature-based temporal encoding. Feature-level analyses revealed interaction signals suggestive of differential emotion-related modulation across groups. In subject-wise evaluation, BESN achieved 83.1% accuracy for HC versus FDE discrimination and 70.6% accuracy for three-class classification (HC versus FDE versus RDE), outperforming conventional machine-learning baselines. Robustness was further supported on an external public resting-state EEG dataset (MODMA), achieving 78.2% accuracy. These results suggest that baseline-anchored, emotion-modulated EEG dynamics combined with path-signature modeling provide an objective and generalizable computational approach for depression-related group discrimination.
14 May 2026
Read appraisal →JMIR mental health
Therapeutic Interventions Targeted at Problematic Use of Digital Technology: Systematic Review and Meta-Analysis of Evidence
BACKGROUND: Problematic use of digital technology has increased across the world. Despite growing research, evidence on treatment effectiveness across digital behaviors remains fragmented. OBJECTIVE: This study aimed to systematically evaluate and compare the effectiveness of therapeutic interventions targeted at problematic use of digital technology across various behavioral domains. METHODS: A systematic review and meta-analysis was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines (PROSPERO: CRD420251052442). Electronic searches of PubMed, Scopus, and Embase (up to April 2025) were conducted. It identified 125 eligible studies, including 73 randomized controlled trials (RCTs), 32 non-RCTs, 14 pre-post studies, and 6 pilot studies. The interventions that were assessed in these studies included psychological therapies, digital or web-based programs, exercise-based interventions, pharmacological treatments, neuromodulation, parent-focused programs, virtual reality-based interventions, educational programs, and multicomponent approaches. Random-effects meta-analyses using standardized mean differences (SMDs) were performed. RESULTS: For problematic internet use, psychological treatments showed a strong effect (effect size=-2.68; P<.001). Digital interventions also showed significant benefit (effect size=-1.16; P<.001). For smartphone addiction, psychological treatments (effect size=-1.49; P<.001) and exercise-based programs (effect size=-3.07; P=.001) showed significant improvement. For gaming disorder, psychological treatments showed improvement (effect size=-1.01; P=.02), but results were mixed. There were limited studies to calculate pooled results for social media addiction, pornography use, gambling, screen time, and over-the-top content watching. No treatment studies were found for problematic over-the-top content watching. High heterogeneity and evidence of small-study effects were observed in several studies. CONCLUSIONS: Overall, structured psychological therapies showed the most consistent benefit. These findings support structured interventions that aim for control of use and reduce cues linked to high use. Evidence remains limited for several emerging digital behaviors. More high-quality studies are needed in clinical settings and for less-studied forms of digital addiction.
14 May 2026
Read appraisal →JMIR aging
Self-Identified Age Cohorts and Personal Experience Sharing in Pseudonymous Online Spaces: Natural Language Processing Analysis of Reddit
BACKGROUND: While older adults' social media use has been widely studied for its instrumental benefits, such as accessing health information or maintaining family ties, research has largely focused on identity-based platforms that mirror offline social networks, leaving pseudonymous, interest-driven environments such as Reddit underexplored. Although older adults actively participate in these spaces to share personal narratives and engage beyond their existing social roles, the literature has yet to center their own voices, with most existing work focusing on caregivers or younger users discussing older adults rather than older adults speaking for themselves. OBJECTIVE: Integrating a life course perspective with the concept of subjective age cohort, this study examines how Reddit users who explicitly self-identify as being in their 50s, 60s, or 70s engage in self-expression and personal experience sharing within pseudonymous online spaces. METHODS: Using data collected via the Reddit application programming interface, this study analyzed posts and comments from 848 self-identified older Reddit users, including 488 future older adults in their 50s (57.5%) and 360 current older adults in their 60s and 70s (42.5%) identified through age-based flairs in the subreddit r/AskOldPeople. A fine-tuned large language model (BLOOM-560m; F1-score=0.96) classified 4,055,275 sentences into three personal experience domains: (1) health and wellness, (2) personal relationships and identity, and (3) professional and financial life. Chi-square analysis compared domain distributions across age groups. BERTopic topic modeling identified thematic patterns within each domain. RESULTS: Of 569,107 personal experience sentences identified, personal relationships and identity comprised the largest share (n=268,212, 47.1%), followed by professional and financial (n=186,768, 32.8%) and health and wellness (n=114,127, 20.1%). Chi-square analysis revealed significant between-group differences (χ22=34.7; P<.001): current older adults shared proportionally more about health and wellness, whereas future older adults shared more about relationships and identity. Topic modeling further revealed qualitatively distinct emphases within each domain. Future older adults' posts frequently discussed menopause, depression, and friendships, while current older adults more frequently addressed chronic illness, aging, and financial security. Both groups used pseudonymity to disclose sensitive or stigmatized topics. CONCLUSIONS: This study demonstrates that older adults' personal experience sharing on pseudonymous platforms is shaped by the simultaneous interplay of life course stage, age cohort background, and platform affordances, rather than by any single factor. Pseudonymous environments such as Reddit enable disclosures of health vulnerabilities, contested social identities, and emotional experiences that are structurally discouraged on identity-based networks, and this has direct implications for how digital inclusion is conceptualized and practiced. Beyond operational literacy, supporting older adults' expressive inclusion in interest-based, pseudonymous online communities represents a meaningful yet underaddressed dimension of digital participation policy.
14 May 2026
Read appraisal →Molecular neurobiology
Gut Microbiota Dysbiosis and Neuroinflammation in Alzheimer's Disease: a Systematic Review of Mechanistic Insights
This systematic review aims to identify microbial species associated with Alzheimer's disease and clarify how they influence Alzheimer's disease pathology, including immune system interactions and blood-brain barrier integrity. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, 18 studies were analyzed, revealing significant differences in gut microbiota composition between patients with Alzheimer's disease and controls. Key findings include reduced beneficial bacteria, such as Faecalibacterium prausnitzii and Roseburia hominis and increased pro-inflammatory species like Escherichia coli. Mechanistically, Escherichia coli produces lipopolysaccharides, triggering the release of pro-inflammatory cytokines such as tumor necrosis factor-alpha. This response correlates with elevated YKL-40 levels and reduced beneficial bacteria like Turicibacter. Additionally, reductions in butyrate-producing bacteria impair blood-brain barrier integrity, while bile acid metabolism disruptions impair signaling pathways, exacerbating amyloid-beta aggregation. These findings highlight the interplay between gut microbiota-derived metabolites and neuroinflammation, suggesting that targeting the gut microbiota for novel diagnostic and therapeutic approaches in Alzheimer's disease.
14 May 2026
Read appraisal →BMC psychiatry
N-acetylcysteine for patients with alcohol use disorder, post-traumatic stress disorder, and their co-occurrence: a systematic review of placebo-controlled randomized trials
INTRODUCTION: Alcohol use disorder (AUD) and post-traumatic stress disorder (PTSD) are thought to be significant global contributors to the burden of mental illness. According to recent WHO epidemiological estimates, 3.9% of people worldwide have experienced PTSD at some point in their lives; this number rises to roughly 5.6% among those who have experienced trauma. NAC has shown promise in reducing PTSD symptoms and cravings in veterans, according to recent trials. Our analysis aims to resolve the conflict between results and determine whether NAC is an effective add-on therapy for PTSD, AUD, and co-occurring PTSD/AUD. METHODS: We conducted this study in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, following our protocol (CRD420251170956). We searched PubMed, Scopus, Cochrane CENTRAL, Embase, EBSCO, and the Web of Science for relevant studies. Only randomized placebo-controlled trials were included. The primary outcomes were severity of PTSD and alcohol craving, while secondary outcomes were anxiety, depression, and alcohol use patterns and biomarkers. RESULTS: Seven randomized, double-blind, placebo-controlled trials were included in this meta-analysis, enrolling a total of 566 participants. There were no significant differences between NAC and placebo in craving (SMD= -0.40, 95% CI [-1.05, 0.25], P = 0.22, I² = 56.1%). Regarding PTSD severity, the PCL scale pooled analysis demonstrated no significant difference between NAC and placebo (SMD = 0.04, 95% CI [-0.40, 0.48], P = 0.8) (I² = 65.9%, P = 0.05), while the CAPS scale pooled analysis demonstrated no significant difference between NAC and placebo (SMD = -0.13, 95% CI [-0.50, 0.24], P = 0.49) (I² = 54.8%, P = 0.109). Leave-one-out sensitivity analysis showed that exclusion of the Back et al. (2025) study resulted in a significant effect favoring NAC (SMD = -0.35, 95% CI [-0.70, -0.01], P = 0.04), with no observed heterogeneity (I² = 0%). CONCLUSION: Our meta-analysis indicates that N-acetylcysteine, when compared to placebo, does not demonstrate consistent efficacy in reducing the primary symptoms of AUD or PTSD in broad patient populations. However, the treatment is relatively safe. A noteworthy signal of potential efficacy for PTSD symptoms emerged in a sensitivity analysis, suggesting that the therapeutic promise of NAC should not be entirely dismissed. CLINICAL TRIAL NUMBER: Not applicable.
14 May 2026
Read appraisal →JMIR mHealth and uHealth
Contribution of Longitudinal Mobile Health Measures in the Dynamic Track of Patients With Major Depressive Disorder: Multiple Centers, Prospective Cohort Study Using Functional Data Analysis and Machine Learning
BACKGROUND: Continuous follow-up for patients with major depressive disorder (MDD) is essential for treatment decisions and a better prognosis. There remains limited evidence regarding the critical issue of depression variation trajectory prediction using mobile health (mHealth) measures. Moreover, the temporal dynamics of mHealth measures have not been fully modeled in previous studies, and the poor patient adherence to mHealth records poses great challenges to the dynamic feature modeling. OBJECTIVE: This study aimed to examine the contribution of mHealth measures in predicting depression variation trajectory for patients with MDD, with full consideration of the temporal dynamics of mHealth measures. METHODS: A total of 229 patients with MDD from a multiple-center, prospective cohort were included. A 12-week follow-up was conducted involving the collection of the Hamilton Depression Rating Scale (HAMD-17), along with patient-reported outcomes (Immediate Mood Scaler and Altman Self-Rating Mania Scale) via mobile devices and sleep duration through wearable wristbands. We used functional data analysis to extract dynamic features from the sparse mHealth records, rather than aggregating the data to a single scalar summary measure through collapsing over time. Subsequently, 3 machine learning models were applied to predict the depression variation trajectory classes based on the baseline characteristics and these extracted dynamic features. RESULTS: Based on the variation of HAMD-17 scores within 12 weeks, the participants were labeled into 4 classes through the k-means algorithm. The classes included stable decline (n=93), fluctuate decline (n=44), fast decline (n=60), and delayed and fluctuate (n=32), in light of the shape of depression trajectories. With both baseline features and dynamic features of the mHealth measures, accuracy rates for the overall data were 54.35%, 60.87%, and 56.52%, for the stable decline patients were 78.95%, 84.21%, and 73.68%, for the nonstable decline patients were 59.26%, 62.96%, and 70.37% based on the 3 machine learning models, respectively. The results were significantly superior to the prediction obtained without mHealth measures (with an overall accuracy below 50%) and only showed a marginal reduction in accuracy relative to the ideal prediction with assessment obtained from clinical visits. Moreover, in the construction of the most accurate prediction model, dynamic features of the Immediate Mood Scaler, the Altman Self-Rating Mania Scale, and sleep duration emerged as the most influential predictors, ranking first, third, and fourth, respectively, in terms of their relative importance. CONCLUSIONS: Longitudinal mHealth measures show potential in depression variation trajectory monitoring for patients with MDD even under poor patient adherence. Our work provides practical help in alleviating the follow-up burden for patients with MDD and validates the effectiveness of mHealth measures in clinical applications.
13 May 2026
Read appraisal →Journal of medical Internet research
Combining Machine Learning Models and Screening to Enhance Suicide Risk Identification for American Indian Patients: Retrospective Cohort Study
BACKGROUND: American Indian and Alaska Native communities experience disproportionately high suicide rates. While machine learning (ML) models leveraging electronic health records have emerged as promising tools for suicide risk identification, the optimal integration of these models with existing screening practices remains unclear. OBJECTIVE: The objective of this study was to compare parallel and serial testing strategies that combine an ML suicide risk model and the Ask Suicide-Screening Questions (ASQ) against using the ASQ alone. To achieve this, we conducted a retrospective secondary analysis of electronic health record data. The cohort consisted of adult emergency department visits at an Indian Health Service facility between October 1, 2019, and October 2, 2021. METHODS: Sensitivity, specificity, predictive values, and 95% CIs were averaged across 10 cross-validated patient-level folds. The final sample included 7897 American Indian patients with 26,896 visits, 824 (3.1%) of which had a positive ASQ result and 102 (0.4%) of which had the outcome of suicide attempt or death within 90 days of the visit. The logistic regression ML model previously developed using Indian Health Service-specific data was operationalized at the 95th and 75th percentiles to evaluate high-risk and medium-risk thresholds, respectively. A sensitivity analysis was performed to evaluate identification approaches across all emergency department visits during this period. RESULTS: The ML medium-risk threshold alone identified the most true positives (sensitivity: 0.782, 95% CI 0.648-0.915; specificity: 0.751, 95% CI 0.725-0.777; positive predictive value [PPV]: 0.012, 95% CI 0.009-0.014; negative predictive value [NPV]: 0.999, 95% CI 0.998-0.999) in comparison to the ML high-risk threshold alone (sensitivity: 0.429, 95% CI 0.287-0.572; specificity: 0.955, 95% CI 0.948-0.961; PPV: 0.035, 95% CI 0.022-0.048; NPV: 0.998, 95% CI 0.997-0.999) or the ASQ alone (sensitivity: 0.178, 95% CI 0.073-0.282; specificity: 0.970, 95% CI 0.968-0.971; PPV: 0.022, 95% CI 0.010-0.034; NPV: 0.997, 95% CI 0.996-0.998). Combining the ML high-risk threshold with the ASQ in series yielded the greatest positive predictive ability (PPV: 0.050, 95% CI 0.014-0.086) at the cost of reduced sensitivity (0.129, 95% CI 0.036-0.221). Finally, the parallel testing approach using the ML medium-risk threshold yielded the greatest sensitivity (0.795, 95% CI 0.671-0.920; specificity: 0.742, 95% CI 0.716-0.767; PPV: 0.012, 95% CI 0.009-0.014; NPV: 0.999, 95% CI 0.998-0.999) without missing any cases identified through screening. CONCLUSIONS: Unlike existing studies that evaluate ML and screening tools in isolation, this study innovates by assessing combined parallel and serial testing strategies in a real-world setting. We demonstrated that, while serial testing maximizes predictive accuracy, it is often infeasible. Instead, parallel testing brings value as a clinical "safety net" to catch at-risk patients missed by standard practices. Ultimately, integrating ML in suicide prevention requires balancing statistical accuracy with setting-specific, real-world workflows.
13 May 2026
Read appraisal →JMIR human factors
The Quality and Characteristics of Digital Mental Health Apps: Mixed Methods Study
BACKGROUND: There are around 20,000 mental health apps available in app stores. The Organisation for the Review of Care and Health Apps (ORCHA), a United Kingdom digital health compliance company, has assessed a number of digital mental health apps with regard to their quality, professional and clinical assurance, data privacy, and user experience. This study analyzes the data that were collected by ORCHA when they assessed mental health apps. OBJECTIVE: This study aimed to examine the characteristics of mental health apps regarding their quality, target users, features, underpinning evidence, and data privacy. METHODS: A dataset comprising ORCHA Baseline Review assessments of over 2000 digital health apps, including 436 mental health apps, was used. This study uses exploratory data analysis to gain insight into the quality and characteristics of mental health apps. Methods such as descriptive and inferential statistics, k-modes clustering, and association rule mining were used to explore the quality of mental health apps as well as reveal insights into the different cost types, target users, app features, data types, and evidence of app content. RESULTS: Information provision, data capture, and data sharing were the most common features within the 436 mental health apps. The examined apps primarily targeted the following groups: adults (n=229, 52.5%), everyone (n=184, 42.2%), and teens (n=135, 31%). The cost of apps has not been linked to the quality of mental health apps, although paid apps or apps with in-app purchases may include additional services. Indicated user acceptance or benefit is the most common type of evidence provided by these mental health apps. A total of 241 (55.3%) apps included a qualified professional in app development, and 251 (57.6%) apps provided evidence within the app that the developer validated any guidance with relevant reliable information sources or references. Usage data and email were the most commonly collected data types. Association rule mining showed that email, IP address, name, and usage data are often co-collected by the same apps. K-modes cluster analysis showed that mental health apps can be categorized into 2 clusters, where one cluster of apps (n=182, 41.7%) collected more data than apps in the other cluster. CONCLUSIONS: Mental health apps are commonly targeted for everyone to use, but many apps are targeted toward teens or adults. Our study suggests that many publicly available mental health apps did not take the precautions (such as the involvement of appropriate health professionals, literature references, or conducting tests) to ensure that their content is valid and research based. Greater effort on behalf of mental health app developers is needed to ensure that the public is provided with high-quality apps. Moreover, our study indicates that the mental health apps that collect more data tend to score better on the ORCHA Baseline Review assessment.
13 May 2026
Read appraisal →Psychological medicine
Risk factors associated with blood dyscrasia during clozapine treatment: a systematic review and meta-analysis
Clozapine is the only effective treatment for treatment-resistant schizophrenia, but concerns over blood dyscrasia and need for monitoring limit its use. Evidence suggests many hematological abnormalities may result from surveillance bias, with agranulocytosis being the primary adverse effect directly induced by clozapine. This systematic review (PROSPERO: CRD42024487199) investigates non-genetic risk factors associated with blood dyscrasia during clozapine treatment, focusing on neutropenia and agranulocytosis. Random-effect meta-analyses were performed on studies reporting quantitative risk data. Due to inconsistent neutropenia definitions, analyses used absolute neutrophil count (ANC) thresholds of <2000/mm3, <1500/mm3, and <500/mm3. Forty-four studies were included in the systematic review, 15 in meta-analyses. No significant association was found between agranulocytosis and female gender (OR = 1.48, 95% CI: 0.92-2.38; p = 0.106) or age (pooled standardized mean difference [SMD] = 0.32, 95% CI: -0.26 to 0.90, p = 0.285), whilst a modest inverse association with clozapine dose (SMD = -0.32, 95%CI: -0.50 to -0.14, p < 0.001) and baseline white cell count (SMD = -0.21, 95%CI: -0.40 to -0.03, p = 0.026) was found. Neutropenia (ANC < 2000/mm3) was positively associated with concomitant psychotropic use (OR = 2.15, 95% CI: 1.13-4.07, p = 0.019). Clozapine rechallenge studies revealed no significant associations with gender, age, duration of initial clozapine trial, or length of discontinuation period prior to rechallenge. No strong predictors of clozapine-associated blood dyscrasia were identified. Findings may be limited by study variability, surveillance bias, and lack of consistent differentiation between agranulocytosis and milder neutropenia, highlighting limitations in current evidence.
13 May 2026
Read appraisal →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 →Archives of women's mental health
Who is screened for perinatal mental health? findings from an academic health system in California.
PURPOSE: To identify sociodemographic characteristics, prenatal healthcare utilization, and health factors associated with completed mental health screenings during pregnancy and postpartum, respectively. METHODS: We examined electronic medical records (EMR) data on documented perinatal mental health screenings from over 6000 patients who delivered singleton infants within one academic healthcare system in California between 2021 and 2023. Dependent variables included having at least one Edinburgh Postnatal Depression Scale (EPDS) score documented in the EMR during (1) pregnancy and (2) postpartum, among those who completed postpartum care within 12 weeks of delivery. We used univariate frequencies to determine rates of completed screenings and logistic regression models to identify patient characteristics associated with completed screenings in each time period. RESULTS: Overall, 67% of patients completed screenings in pregnancy and 56% completed screenings in postpartum. In pregnancy, patients who were White, multiparous, did not prefer English, began prenatal care in the third trimester, and completed 10 or fewer prenatal care visits each had lower odds of completed screenings than their counterparts. In postpartum, patients who were White, privately insured, had vaginal deliveries, and had term births (at least 37 weeks of gestation) each had lower odds of completed screenings than their counterparts. CONCLUSIONS: Although California law requires perinatal mental health screening in pregnancy and postpartum , the identified inequities in screening suggest additional efforts are needed to ensure all perinatal patients are screened for mental health in accordance with the law. These efforts should be tailored to the populations least likely to be screened in order to achieve universal perinatal mental health screening.
13 May 2026
Read appraisal →Archives of women's mental health
Influences of adverse childhood experiences and weekly pregnancy stress on postpartum mental health symptoms: a machine learning examination
PURPOSE: The goal of this study was to understand how the timing, level, and fluctuation of pregnancy stress affected women's postpartum mental health while also considering their experiences of early life stress (adverse childhood events; ACEs). We hypothesized that overall stress level, as well as stress during particular pregnancy weeks, would influence postpartum mental health. We further hypothesized that fluctuations in weekly perceived stress levels across pregnancy would influence postpartum mental health among women who had experienced ≥ 4 ACEs. METHODS: We collected information on ACEs and weekly stress data from our participants during pregnancy (15 weeks until parturition). Women's depressive and anxiety symptoms were assessed at six-months postpartum. We employed a machine learning approach to explore the predictive utility of timing, level, and fluctuation of pregnancy stress on postpartum mental health symptoms. RESULTS: For all women, higher average stress level across all weeks of pregnancy predicted greater postpartum depressive and anxiety symptoms. Additionally, women with a history of ≥ 4 ACEs were susceptible to stress levels in weeks 20 of pregnancy (depressive symptoms only) and 26 (depressive and anxiety symptoms). Lastly, greater variability in stress levels across pregnancy was also associated with greater depressive symptoms for the women with ≥ 4 ACES. CONCLUSION: In addition to overall stress level, timing of pregnancy stress and fluctuations in stress levels across pregnancy are associated with postpartum mental health for women with a history of substantial ACEs, with implications for focused mental health interventions during pregnancy for these women.
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 →Journal of medical Internet research
Efficacy of Technology-Based Interventions on the Reduction of Loneliness: Systematic Review and Meta-Analysis
BACKGROUND: Loneliness is a widespread public health concern linked to increased risks of health problems. As populations age, the demand for effective interventions to mitigate loneliness continues to grow. OBJECTIVE: This meta-analysis aimed to examine the effectiveness of technology-based interventions in reducing loneliness. METHODS: A systematic literature search was conducted in Web of Science, PsycInfo, PubMed, Scopus, Google Scholar, Embase, and the Cochrane Library in August 2024. We included randomized controlled trials that examined the effectiveness of technology-based interventions compared with any control group in reducing loneliness across all age groups. Nonrandomized studies, qualitative research, and studies lacking sufficient statistical data for effect size calculation were excluded. After screening 1089 records, 7 studies involving 580 participants met the inclusion criteria. Data were extracted by 3 independent reviewers, with discrepancies resolved by a fourth reviewer. The risk of bias was assessed using the Cochrane risk-of-bias tool. A random-effects model was used to synthesize effect estimates, with standardized mean differences as the primary effect size metric. Heterogeneity was assessed using the Q statistic and I² index, and a prediction interval was calculated to estimate the expected range of true effects. RESULTS: We found a small and statistically nonsignificant overall effect of technology-based interventions on loneliness (pooled standardized mean difference=-0.21, 95% CI -0.59 to 0.17; 95% prediction interval -1.14 to 0.63). Substantial between-study variability was present (I²=57%; τ²=0.08; τ=0.28), and the prediction interval indicated that true effects in future studies may range from substantial reductions to moderate increases in loneliness. Differences between intervention types could not be examined due to the limited number of eligible studies. The Egger test showed significant funnel plot asymmetry (t5=-5.47; P=.003). However, since the trim-and-fill method identified no missing studies, the asymmetry is unlikely to be fully explained by publication bias. CONCLUSIONS: Unlike previous reviews, which focused primarily on older populations or included nonrandomized studies, this meta-analysis provides a rigorous synthesis of only randomized controlled trials across the lifespan. Our findings do not provide evidence for a reliable reduction in loneliness following technology-based interventions. Moderate heterogeneity indicates that effects differ across studies, and the limited number of eligible trials prevented the analysis of potential moderators. By incorporating robust prediction intervals and a broader age demographic, this study offers a more comprehensive view of the variability of intervention outcomes compared with existing literature. The wide prediction interval suggests that intervention effects may vary across settings. In practice, technology-based approaches should be viewed as supportive tools that require careful tailoring rather than universal solutions to loneliness.
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 →JMIR medical informatics
Resource Use Patterns in US Telehealth Services: Machine Learning and Clustering Analysis Across 4 Specialties
BACKGROUND: The expansion of telehealth services, particularly during the COVID-19 pandemic, has transformed health care delivery in the United States. Telehealth promises greater access and resource efficiency by reducing wait times and appointment lengths, especially in specialties like psychiatry, behavioral health, bariatrics, and sleep medicine. However, disparities exist in adoption based on demographics, geography, and socioeconomic status, raising concerns about equitable access and optimal resource use. OBJECTIVE: This study aims to evaluate how telehealth impacts health care resource use across 4 specialties by examining 2 key metrics: patient-to-provider ratios and appointment durations. It seeks to understand how factors such as patient demographics, facility characteristics, and social determinants influence telehealth adoption and efficiency using a national dataset spanning from 2018 to 2023. METHODS: We analyzed a deidentified dataset from Epic Cosmos, covering outpatient visits across 48 US states (2018-2023). After data preprocessing and feature engineering, we applied 3 machine learning (ML) models (random forest, extreme gradient boosting, and deep neural networks) to predict resource use. Using the model performing the best, feature importance was assessed using Shapley Additive Explanations values. We then used k-means clustering to group facilities into clusters per specialty. Comparative analyses were conducted to evaluate differences in use among clusters, during and after the pandemic. RESULTS: Telehealth use peaked in 2020 and has remained above prepandemic levels since then. In 2018-2023, telehealth adoption reached 36.9% (4,543,021/12,311,710) in psychiatry, 23.9% (5,321,099/22,264,013) in behavioral health, 21.2% (924,333/4,360,061) in bariatrics, and 16.8% (851,803/5,070,256) in sleep medicine. Telehealth visits were consistently shorter than office visits (mean reduction 12.24 minutes; SD 3.33 minutes; P=.18), while patient-to-provider ratios varied significantly across specialties. Among ML models, extreme gradient boosting regression achieved the best performance (patient-to-provider ratios: R2=0.96-0.99; appointment durations: R2=0.61-0.69). Shapley Additive Explanations analysis identified visit type, telehealth use, facility size, rurality, and Social Vulnerability Index household vulnerability as the strongest predictors. Comparative analyses showed significant differences across clusters (all P<.05). CONCLUSIONS: Telehealth has become a sustainable component of health care, enhancing access and efficiency across both rural and urban areas. However, its impact varies across specialties and regions, highlighting the need for targeted strategies such as staffing support for vulnerable populations, infrastructure investments in rural facilities, and reimbursement models that reflect telehealth's resource use. This study provides robust evidence from ML and clustering analyses, demonstrating how telehealth shapes resource use and offering actionable insights for equitable and sustainable integration.
12 May 2026
Read appraisal →BMJ mental health
Mental health in university students: an umbrella review grading the evidence for psychosocial interventions
QUESTION: Psychological distress is increasingly prevalent among university students, raising concerns about their mental health. This review aimed to assess the strength and credibility of evidence on the efficacy of psychosocial interventions for improving mental health in this population. STUDY SELECTION AND ANALYSIS: We conducted an umbrella review of systematic reviews (SRs) with meta-analyses of randomised controlled trials assessing psychosocial interventions among university students. Searches were performed in Medline, PubMed, Cochrane Central, PsycINFO, CINAHL, Epistemonikos and Campbell Collaboration from 1 January 2014 to 1 February 2026. Data on intervention type, comparator, outcome and study-level effect sizes were analysed using inverse-variance random-effects models with restricted maximum likelihood estimation of between-study heterogeneity (τ²). Strength of associations was evaluated according to umbrella review criteria, reporting quality using A MeaSurement Tool to Assess systematic Reviews 2 (AMSTAR-2) and certainty of evidence using Grading of Recommendations Assessment, Development and Evaluation (GRADE). FINDINGS: A total of 34 SRs were identified. After excluding overlapping syntheses, 28 unique meta-analyses from 12 SRs were included. Mindfulness-based interventions versus inactive controls for distress (standardised mean difference (SMD) -0.40; 95% CI -0.53 to -0.28) showed highly suggestive evidence (class II; GRADE: low; AMSTAR: high). Seven associations provided suggestive evidence (Class III): mindfulness-based interventions versus inactive controls for anxiety (SMD -0.54; 95% CI -0.81 to -0.27) and depression (SMD -0.52; 95% CI -0.65 to -0.39); cognitive-based interventions versus inactive controls for anxiety (SMD -0.48; 95% CI -0.62 to -0.34) and depression (SMD -0.60; 95% CI -0.75 to -0.45); cognitive-based interventions versus active controls for well-being (SMD -0.30; 95% CI -0.42 to -0.18); positive psychology interventions versus inactive controls for distress (SMD -0.22; 95% CI -0.35 to -0.10); and multimodal interventions versus inactive controls for distress (SMD -0.85; 95% CI -1.32 to -0.87). The remaining associations were weak or non-significant, generally supported by low or very low certainty and mostly low or very low reporting quality. CONCLUSIONS: The most robust evidence emerged for mindfulness and cognitive-based interventions, with some support also for positive psychology approaches. These interventions appear to improve depression, anxiety, distress and well-being among university students, though further rigorous research is needed. PROSPERO REGISTRATION NUMBER: CRD42023493618.
12 May 2026
Read appraisal →JMIR human factors
Persuasive Systems Design Features of Smartphone Apps for Psychosis: Systematic Review
BACKGROUND: It is unclear why some smartphone apps designed for people with psychosis are engaging, while others are not. One possible explanation is the apps' persuasive features and the operationalization and implementation of these features. OBJECTIVE: This systematic review set out to quantify and describe the persuasive features used in smartphone apps for psychosis, investigate whether there was any association between persuasive features and attrition or adherence rates. and document the quality of the included apps. METHODS: We searched electronic databases PsycINFO, PubMed, and Google Scholar for eligible papers published between the years of 2013 and 2025. Hand searches of reference lists were completed. Apps were selected if they were designed for people with psychosis and there were published empirical studies investigating the apps. Two reviewers from the review team (TT, JP, DK, and SD) independently screened papers and extracted data on adherence and attrition, as well as coded papers for evidence of persuasive features according to the persuasive systems design (PSD) model. Available data were synthesized descriptively and narratively. We attempted to access apps via app stores or by correspondence with the research team. RESULTS: We found 22 apps for psychosis, with 30 associated published papers. The persuasive features were as follows: personalization (18 apps); reminders (15 apps); suggestions (11 apps); tunneling and self-monitoring (10 apps); reduction (9 apps); liking (8 apps); social role (6 apps); rehearsal, praise, and similarity (5 apps); rewards (4 apps); simulation, real-world feel, and social learning (3 apps); surface credibility and normative influence (2 apps); and trustworthiness, social comparison, and social facilitation (1 app). Expertise, authority, third-party endorsements, verifiability, cooperation, competition, and recognition were present in zero apps. Features in the categories of primary task support and dialogue support were well represented, while social support and system credibility support were underused. It was found that there was no association between the number of persuasive features and attrition; an association between persuasive features and adherence could not be assessed. The quality of the apps could not be judged due to 20 of the 22 apps being inaccessible either through the research papers' authors or through app stores. CONCLUSIONS: Our findings indicate that in psychosis apps there is potential to include a broader range of persuasive features, which might maximize engagement. Psychosis apps may benefit from incorporating more features that leverage the persuasive impact of having users interact (social support) and incorporating features that emphasize system credibility and trustworthiness. Further studies could determine whether an increase in the number of persuasive features will impact app engagement and which features are most impactful in this context.
12 May 2026
Read appraisal →Scientific reports
Hypnotic safety suggestions reduce cortisol awakening response and morning heart rate in daily life.
With stress being causally linked to the most frequently occurring mental and physical health problems, effective coping with acute stress is essential for promoting overall well-being and building long-term stress resilience. However, most interventions aimed at improving stress regulation require extended periods of training and practice. In a previous study, we demonstrated that the Jena Safety Anchor-a brief, hypnosis-based intervention-effectively reduced subjective stress responses in a controlled laboratory setting using the Trier Social Stress Test (TSST). The present study examined whether this effect extends to everyday life. A total of 80 participants (n = 40 female) were randomly assigned to either a hypnosis group or a control group (n = 40 per group). Over the course of two weeks, all participants collected saliva samples at home to assess their cortisol awakening response (CAR), an indicator of anticipatory stress, reflecting the body's preparation for expected demands upon waking. Additionally, all participants continuously recorded their heart rate via smartwatches during the two data collection weeks. The hypnosis group received a single guided session of the Jena Safety Anchor at the end of the first week, while the control group came to the laboratory but received no intervention. CAR was measured via three saliva samples taken within one hour after waking on specified days. Morning heart rate was measured between 6 and 9 a.m. Results revealed a significant reduction in CAR and morning heart rate in the hypnosis group during the second week, following the intervention. In contrast, no change in CAR and morning heart rate was observed in the control group. These findings show that a single session of the Jena Safety Anchor can effectively reduce physiological stress anticipation in the morning. This points to its potential as a rapid, low-effort intervention for enhancing stress coping and fostering resilience. It offers a promising approach to mental health promotion that is both time-efficient and easily integrated into everyday routines.
12 May 2026
Read appraisal →Journal of speech, language, and hearing research : JSLHR
Vocal Biomarkers of Childhood Trauma: A Machine-Learning Approach to Speech Analysis
PURPOSE: Childhood trauma can disrupt communication, yet early signs often go unrecognized in regions affected by ongoing war, where immediate physical needs take precedence. Vocal biomarkers-acoustic features linked to emotional and motor regulation-offer a promising, noninvasive means of detecting trauma-linked speech disruptions. This study applied a hybrid framework to distinguish trauma exposure in Arabic-speaking children living amid active conflict. The aim was to support scalable, speech-based tools for early trauma identification in low-resource, humanitarian settings. METHOD: We analyzed 200 publicly available recordings of spontaneous speech from Arabic-speaking girls (ages 8-12 years): 100 trauma-exposed participants from Gaza (Palestinian) and 100 non-exposed controls from Jordan. Core acoustic features (fundamental frequency [F0], jitter, shimmer, harmonics-to-noise ratio [HNR], voice onset time [VOT], first formant, second formant) informed statistical testing and theory-driven composite indices. Exploratory features-including Mel-frequency cepstral coefficients and eGeMAPSv02 descriptors-were used to train binary classification models. Three classifiers (random forest, ridge regression, and logistic regression) were evaluated using nested cross-validation and bootstrap resampling. Composite indices were combined into a 0-10 Trauma Risk Score. Generalizability was assessed using an independent Lebanese cohort (n = 80), with the trained classifier applied using fixed exploratory features and preprocessing parameters. Core features were tested post hoc for cross-cohort stability. RESULTS: Trauma-exposed children showed reduced F0 and HNR, elevated shimmer and jitter, and prolonged VOT (Cohen's d > 1.2). Binary classification models achieved strong performance (area under the curve [AUC] = .89-.92); logistic regression reached AUC = .996 under cross-validation. Composite indices (AUCs > .90) stratified 68% into Moderate/High Trauma Risk. Lebanese validation confirmed generalizability, with theory-driven features showing stable predictive patterns. CONCLUSIONS: Vocal biomarkers reliably distinguished trauma exposure in Arabic-speaking children using a simple logistic regression model. This strong performance highlights the potential of speech-based tools as scalable, noninvasive methods for early trauma detection. Further validation is needed to support their use in diverse humanitarian and conflict-affected settings.
12 May 2026
Read appraisal →Palliative & supportive care
Manic and hypomanic states in cancer patients: A systematic review
OBJECTIVES: While there have been reports on the relationship between cancer and depression, reports on the association between cancer and manic states, a reciprocal state of depression, have been relatively few. Therefore, we conducted a systematic review on the relationships between cancer and manic states, focusing on their etiology, clinical course, and impact on cancer treatments. METHODS: A systematic review was conducted using four electronic databases, following the PRISMA guidelines. The scope of the study included research on manic or hypomanic states associated with cancer in patients with no prior history of mental illness, published from 1950 to August, 2021. The study protocol was registered with PROSPERO (CRD42020182372). RESULTS: Fifty-six studies, including 67 cases, were identified. The etiology of manic states in cancer patients was classified into organic, drug-induced, and psychogenic, with steroids being the most predominant causative agent. Approximately half of the patients discontinued cancer treatment following the onset of manic states. This was associated with a low rate of pharmacological treatment during the acute and maintenance phase of mania. The onset of manic states was most frequent during cancer treatment; however, about 15% of the cases exhibit manic symptoms before cancer diagnosis. SIGNIFICANCE OF RESULTS: This systematic review illustrated the clinical characteristics of manic state regarding differences in the etiology, timing of onset, pharmacological treatments, duration to remission, recurrence, and impact on cancer treatment. Manic states, which are comorbid with cancer, have significant clinical impacts on cancer prognosis. Therefore, appropriate pharmacological treatment for manic states is critical to consolidate appropriate cancer treatment. A substantial proportion of patients exhibit manic symptoms prior to the diagnosis of cancer, warranting further investigation into the possibility of the concept of "premonitory mania."
12 May 2026
Read appraisal →Journal of computer-aided molecular design
NeuroPred-GMC: a dual-branch deep learning architecture for neuropeptide prediction based on gated dilated convolutional network and multi-scale convolutional network
Neuropeptides are multifunctional signaling molecules in the nervous system. By modulating synaptic transmission and integrating physiological systems, they influence a broad range of functions from pain perception to emotional regulation. Predicting neuropeptides can rapidly expand the library of potential therapeutic targets, thereby providing novel candidate molecules for drug development in areas such as analgesics, anti-anxiety medications, and weight-loss drugs. Traditional experimental methods are extremely time-consuming, labor-intensive, these promising alternative computational methods have emerged. In this study, a dual-branch deep learning architecture for neuropeptide prediction known as NeuroPred-GMC are built up based on gated dilated convolutional network with ESM-2 feature representation and multi-scale convolutional network with Prot-T5 feature representation. Dilated convolution exponentially enlarges the receptive field via increased dilation rates, gating mechanism enables dynamic, selective feature enhancement and noise suppression, and multi-scale convolution captures multi-level contextual information. On the independence test set, the accuracy of 93.24%, Sn of 93.69%, Sp of 92.79%, Pre of 92.86%, MCC of 0.8649 and the auROC of 0.9667 are obtained. The experimental results through cross-validation and independent test demonstrate that the proposed model has good robustness and generalizability, and can serve as a supplemental candidate predictor. The source datasets and codes can be freely available at https://github.com/yunyunliang88/NeuroPred-GMC .
11 May 2026
Read appraisal →Behavioral and brain functions : BBF
Neuropeptide S modulation of learning and memory: a systematic review
BACKGROUND: In the last two decades, Neuropeptide S (NPS) has been identified as a key bioactive peptide in the mammalian brain, influencing fear, anxiety, wakefulness, reward, and learning. While some reviews have addressed its role in reward-seeking and anxiety, few have addressed its particular role in learning and memory. The neuropeptide S receptor 1 is highly expressed in key areas for learning processing, such as the hippocampus, cortex, thalamus, and amygdala. This review aims to examine evidence from human and animal studies that focused on the NPS system's role in modulating learning and memory. A special focus is given to experiments addressing the impact of NPS on associative learning leading to addiction and in fear conditioning, pointing to its potential therapeutic value in associated pathologies. MAIN BODY: An advanced search was conducted using the databases PubMed, Google Scholar, Web of Science, and Scopus, focusing on memory and Neuropeptide S. The reviewed data suggest that NPS modulation occurs at all memory phases, including acquisition, consolidation, and retrieval, and in extinction learning, whether motivated by appetitive or aversive stimuli. The summarized evidence shows that the NPS system interferes with working and short-term memory, mitigates learning impairments, enhances spatial and object memory consolidation, supports fear extinction learning and inhibitory avoidance consolidation, and reinstates drug-seeking behaviors. The NPS system closely interacts with key neuromodulators, including orexinergic, dopaminergic, and noradrenergic systems, in influencing memory. CONCLUSION: The Neuropeptide S system emerges as a critical modulator of memory processes. The NPS signaling may preferentially influence learning that involves emotionally or motivationally relevant stimuli. This highlights the NPS system's potential as a target for therapeutic interventions for particular memory impairments.
11 May 2026
Read appraisal →Journal of medical Internet research
Characterizing Physical Activity Trajectories Preceding Incident Major Depressive Disorder Diagnosis With Consumer Wearable Devices in the All of Us Research Program: Retrospective Nested Case-Control Study
BACKGROUND: Low physical activity (PA) is a well-established risk factor for major depressive disorder (MDD). However, the temporal dynamics of PA preceding an incident clinical diagnosis of MDD remain poorly characterized, particularly using long-term, objective measures collected in real-world settings. OBJECTIVE: This study aimed to characterize trajectories of wearable-measured PA during the year preceding incident MDD diagnosis and identify the timing of within-person changes. METHODS: We conducted a retrospective nested case-control study using linked electronic health record and wearable (Fitbit) data from the All of Us Research Program. Adults with at least 6 months of valid Fitbit PA data in the 12 months preceding diagnosis were included. Incident MDD cases were identified based on a first electronic health record-recorded diagnosis and matched to MDD-free controls on age, sex, BMI, and calendar time of diagnosis, with up to 4 controls per case. Daily steps and moderate to vigorous PA (MVPA) were aggregated into monthly averages. Linear mixed-effects models were used to compare prediagnostic PA trajectories between cases and controls over a retrospective time scale from -12 to 0 months. Among cases, within-person contrasts were used to identify when PA levels first showed statistically significant deviations relative to levels observed 12 months before diagnosis. Exploratory analyses assessed heterogeneity by demographic factors. RESULTS: The analytic cohort included 4104 participants (n=829, 20.2% incident MDD cases and n=3275, 79.8% matched controls; n=3355, 81.7% women; median age 48.4, IQR 36.3-61.3 years). Compared with controls, individuals who developed MDD exhibited consistently lower overall PA and significant downward trajectories in both daily steps and MVPA during the year preceding diagnosis (global trajectory tests; P<.001 for both outcomes). Differences widened progressively over time, indicating accelerating declines as diagnosis approached. Among cases, statistically significant changes in daily step counts emerged approximately 4 months before diagnosis (-145, 95% CI -253 to -37 steps vs month -12; P=.02) and reached -428 (95% CI -531 to -326) steps at diagnosis (P<.001). Declines in MVPA emerged approximately 5 months before diagnosis (-2.48, 95% CI -4.32 to -0.64 minutes; P=.02) and reached -5.61 (95% CI -7.35 to -3.86) minutes at diagnosis (P<.001). Furthermore, exploratory analyses suggested heterogeneity in prediagnostic trajectories across demographic subgroups, including steeper declines among men, more pronounced reductions in activity intensity at older ages, and persistently lower activity levels with flatter trajectories among individuals with obesity. CONCLUSIONS: Unlike prior studies lacking objective PA assessment before MDD diagnosis, this study linked wearable and clinical data to characterize long-term prediagnostic trajectories in real-world settings. We observed sustained within-person declines emerging 4 to 5 months before diagnosis, providing insights into temporal dynamics preceding clinical recognition. These findings suggest that wearable-based monitoring may offer scalable early signals for risk stratification, prevention, and intervention for MDD.
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 →Neurosurgical review
Deep brain stimulation for clozapine-resistant schizophrenia: a systematic review of target-specific outcomes and stereotactic technical considerations
Treatment-resistant schizophrenia (TRS) affects approximately 20-30% of patients, and a substantial proportion develop clozapine-resistant schizophrenia (CRS). Deep brain stimulation (DBS) has emerged as a potential neurosurgical intervention targeting dysfunctional cortico-striato-limbic circuitry. However, technical heterogeneity and limited clinical data constrain interpretation of outcomes. This systematic review was conducted in accordance with PRISMA guidelines and prospectively registered in PROSPERO (CRD420251080715). Seven electronic databases were searched from inception through January 31, 2025. Clinical studies investigating DBS in TRS or CRS were included. Methodological quality was assessed using Joanna Briggs Institute (JBI) tools. Data extraction emphasized stereotactic targeting methods, hardware configurations, stimulation parameters, and clinical outcomes. The total number of study is 6. We excluded paper by Manssuer 2023. The total study population comprises 21 patients. Seven studies involving 21 patients met inclusion criteria. Targets included the nucleus accumbens (NAcc; n = 11), substantia nigra (SNr; n = 1), habenula (HB; n = 2), subgenual cingulate (SCG; n = 4), subgenual anterior cingulate cortex (sgACC; n = 3). Reported PANSS total score changes ranged widely (11%-85.7%), reflecting substantial inter-individual variability and methodological limitations. Surgical complications occurred in 3 of 21 patients (14.2%), including infection and hemorrhage. All cases utilized open-loop stimulation and conventional cylindrical leads. Current evidence suggests a preliminary therapeutic signal for DBS in highly selected CRS patients, particularly with NAcc targeting. However, conclusions remain limited by small sample sizes, technical heterogeneity, and absence of controlled trials. Future investigations should prioritize standardized stereotactic reporting, volumetric lead reconstruction, and long-term safety assessment within specialized neurosurgical research settings. PROSPERO CRD420251080715.
10 May 2026
Read appraisal →Lancet (London, England)
Once-weekly semaglutide versus placebo in patients with alcohol use disorder and comorbid obesity: a randomised, double-blind, placebo-controlled trial
BACKGROUND: Alcohol use disorder accounts for 5% of deaths worldwide annually, and there is an urgent need for new therapeutic interventions. Preclinical and initial human studies indicate that the GLP-1 receptor agonist semaglutide might reduce alcohol drinking. This study evaluated the efficacy of semaglutide once-weekly in treatment-seeking patients with alcohol use disorder and comorbid obesity. METHODS: In a 26-week, single-centre, randomised, double-blinded, placebo-controlled trial, treatment-seeking participants with moderate to severe alcohol use disorder and comorbid obesity were assigned (1:1) to receive once-weekly semaglutide (2·4 mg subcutaneously) or placebo (saline subcutaneously), in addition to standard cognitive behavioural therapy. The primary endpoint was a reduction in the number of heavy drinking days assessed after 26 weeks of intervention, analysed with an ANCOVA model. Analysis adhered to the intention-to-treat principle, and missing outcome data were addressed using multiple imputations. Safety was assessed in all treated patients. The trial is registered at ClinicalTrials.govNCT05895643, and is complete. FINDINGS: From June 10, 2023, to Feb 4, 2025, 108 participants (53 women and 55 men) were enrolled, with 54 participants in each of the semaglutide and placebo treatment groups, and all were included in the data analysis. Overall, 88 participants (81%) completed the full intervention. Semaglutide was associated with a reduction in heavy drinking days (-41·1 percentage points from baseline, 95% CI -48·7 to -33·5) compared with placebo (-26·4, -34·1 to -18·6; estimated treatment difference -13·7 percentage points, -22·0 to -5·4; p=0·0015), and had substantial effects on multiple secondary alcohol-related and somatic outcomes. Adverse events were transient, generally mild to moderate gastrointestinal effects, and occurred more frequently in the semaglutide group. INTERPRETATION: Semaglutide showed robust therapeutic effects in treatment-seeking participants with obesity and alcohol use disorder and this trial supports previous preclinical and clinical findings suggesting GLP-1 receptor agonists as a potential novel treatment target for alcohol use disorder. FUNDING: The Research Foundation, Mental Health Services (Capital Region of Denmark), the Novo Nordisk Foundation, the Novavi Foundation, the Hartmann Foundation, and the Augustinus Foundation.
10 May 2026
Read appraisal →Medical image analysis
Transfer learning from 2D natural images to 4D fMRI brain images via geometric mapping
Functional magnetic resonance imaging (fMRI) allows real-time observation of brain activity through blood oxygen level-dependent (BOLD) signals and is extensively used in studies related to sex classification, age estimation, behavioral measurements prediction, and mental disorder diagnosis. However, the application of deep learning techniques to brain fMRI analysis is hindered by the small sample size of fMRI datasets. Transfer learning offers a solution to this problem, but most existing approaches are designed for large-scale 2D natural images. The heterogeneity between 4D fMRI data and 2D natural images makes direct model transfer infeasible. This study proposes a novel geometric mapping-based fMRI transfer learning method that enables transfer learning from 2D natural images to 4D fMRI brain images, bridging the transfer learning gap between fMRI data and natural images. The proposed Multi-scale Multi-domain Feature Aggregation (MMFA) module extracts effective aggregated features and reduces the dimensionality of fMRI data to 3D space. By treating the cerebral cortex as a folded Riemannian manifold in 3D space and mapping it into 2D space using surface geometric mapping, we make the transfer learning from 2D natural images to 4D brain images possible. Moreover, the topological relationships of the cerebral cortex are maintained with our method, and calculations are performed along the Riemannian manifold of the brain, effectively addressing signal interference problems. The experimental results based on the Human Connectome Project (HCP) dataset demonstrate the effectiveness of the proposed method. Our method achieved state-of-the-art performance in sex classification, age estimation, and behavioral measurement prediction tasks. Moreover, we propose a cascaded transfer learning approach for depression diagnosis, and proved its effectiveness on 23 depression datasets. In summary, the proposed fMRI transfer learning method, which accounts for the structural characteristics of the brain, is promising for applying transfer learning from natural images to brain fMRI images, significantly enhancing the performance in various fMRI analysis tasks.
10 May 2026
Read appraisal →Asian journal of psychiatry
Machine learning prediction model for delirium after heart valve replacement with cardiopulmonary bypass: A large-scale cohort study
OBJECTIVE: Postoperative delirium (POD) is a common and severe complication following heart valve replacement (HVR) with cardiopulmonary bypass (CPB), associated with poor outcomes. Accurate early prediction is crucial for targeted prevention. This study aimed to develop and externally validate machine learning (ML) models for predicting POD in this high-risk population. METHODS: We conducted a retrospective cohort study involving 1076 adult patients who underwent HVR with CPB between January 2018 and December 2022. POD was assessed using the CAM-ICU. Perioperative factors were analyzed to develop a traditional logistic regression-based nomogram and four ML models. The best-performing model was then tested on an independent external validation cohort of 281 patients. RESULTS: The incidence of POD was 25.1% (270/1076) and was associated with significantly increased mortality (11.9% vs. 0.2%, P < 0.001) and prolonged hospitalization. In the internal test set, the Random Forest (RF) model demonstrated the highest predictive performance with an AUROC of 0.854 (95% CI, 0.764-0.945), outperforming the traditional nomogram (AUROC: 0.754). The RF model maintained robust performance in the external validation cohort, achieving an AUROC of 0.793. Model interpretation using SHAP analysis identified postoperative awakening time (PAT), postoperative mechanical ventilation time (PMVT), and postoperative reintubation (POR) as the most influential predictors. CONCLUSIONS: Machine learning models, particularly Random Forest, provide a robust and generalizable tool for the early prediction of POD after HVR. These models outperform traditional statistical approaches and offer interpretable insights, highlighting PAT as a critical, modifiable target for perioperative management to mitigate POD risk.
9 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 →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 →Journal of applied gerontology : the official journal of the Southern Gerontological Society
Anomaly Detection Technologies for Dementia Care: Monitoring Goals, Sensor Applications, and Trade-offs in Home-Based Solutions-A Narrative Review
Anomaly detection technologies are increasingly used to monitor people living with dementia (PLwD) in home settings, addressing critical behaviors such as wandering, sleep disturbances, and agitation. This narrative review examines technologies used for detecting behavioral anomalies, the activities they monitor, and the trade-offs between their benefits and limitations. A systematic search across MEDLINE, IEEE Xplore, ACM Digital Library, and Web of Science identified 78 studies, categorized through thematic analysis. Three primary motivations emerged: early diagnosis, safety monitoring, and reducing caregiver stress while promoting autonomy. Technologies include GPS tracking, wearables, environmental sensors, and smart home systems, each with benefits like real-time alerts and non-intrusive monitoring but also challenges such as user compliance, false positives, and privacy concerns. While these systems enhance safety and autonomy, improving sensor accuracy, integrating AI for personalized interventions, and addressing ethical concerns are essential for long-term effectiveness and supporting the well-being of both PLwD and caregivers.
5 May 2026
Read appraisal →Journal of psychiatric research
Predicting treatment response in psychosis using fMRI: A comprehensive review
In recent years, the use of functional Magnetic Resonance Imaging (fMRI) methods to predict treatment response in schizophrenia (SCZ) through statistical and machine learning (ML) algorithms has increased. We conducted a comprehensive literature review to assess the role of various fMRI measures in predicting pharmacological treatment response in psychosis. Literature available on PubMed from January 1990 to December 2023 was reviewed, and 21 fMRI studies were included. The results suggest that many studies have employed ML techniques, which may enhance the accuracy of treatment outcome predictions. Additionally, several studies utilizing resting-state fMRI have identified potential associations of functional connectivity patterns across multiple large-scale networks, including the default mode network (DMN), the salience network (SN), the central executive network (CEN), and sensory-motor circuits. These findings suggest that altered connectivity within and between these networks may be relevant for personalized treatment strategies in patients with psychosis, although further investigation is needed to confirm their predictive value. Future research should focus on developing robust and generalizable models to more reliably optimize treatment outcomes in psychosis.
5 May 2026
Read appraisal →International journal of psychophysiology : official journal of the International Organization of Psychophysiology
Task-evoked brain network dynamics underlying cognitive control using source-localized EEG and machine learning
OBJECTIVE: Functional connectivity (FC) coordinates brain activity during cognitive tasks, yet the influence of demographic variables and health factors on task-related dynamic FC remains underexplored. We examined FC within and between seven core brain networks: Default Mode (DMN), Ventral Attention (VAN), Somatomotor (SMN), Dorsal Attention (DAN), Frontoparietal (FPN), Visual (VIN), and Limbic (LIN) during a modified Eriksen flanker task. We aimed to identify distinct network configurations linked to varying cognitive control demands and assess the influence of demographic and health factors. METHODS: Secondary analyses of source-localized EEG data from 255 adults (mean age 38.97 ± 11.65 years; BMI 30.21 ± 9.29 kg/m2) were conducted. A hidden Markov model extracted spatiotemporal dynamics, quantifying within- and between-network correlations. Extreme Gradient Boosting with recursive feature elimination identified connectivity metrics predicting reaction time (RT) of high performers (>80% accuracy). Regression models assessed effects of age, sex, BMI, and income on task-related FC. RESULTS: Classification accurately predicted RT in both congruent (AUC = 0.81) and incongruent (AUC = 0.86) trials. Key FC features included VAN-LIN, VIN-VAN, and SMN-DAN for congruent trials; SMN-DMN, LIN-FPN, and VIN-DAN for incongruent trials. Age was linked to reduced FC, while higher BMI showed modest positive FC associations in LIN-FPN and VIN-DAN. Sex and income were not significant predictors of RT or FC. CONCLUSIONS: These findings reveal the relevance of specific dynamic network interactions in cognitive control and highlight the need to consider age and BMI as contributors to brain connectivity during task performance.
4 May 2026
Read appraisal →Social science & medicine (1982)
Imaginaries of digital health: Unpacking the platformization of mental health through users' practices
The platformization of healthcare is reshaping the organization of mental health services, redefining access, relationships, and meanings of care. This paper examines how users experience and negotiate the platformization of mental health through the case of Unobravo, an Italian digital platform offering online psychotherapy. Drawing on Science and Technology Studies and digital health literature, the study explores how platform imaginaries of efficiency, accessibility, and personalization are enacted and contested in everyday practices. Adopting a mixed-methods design, the research combines qualitative interviews (N = 12) with a quantitative survey (N = 3870) to analyse users' narratives and patterns of engagement. Findings reveal a persistent tension between the promise of algorithmic efficiency and the realities of situated practice. While users value the flexibility and accessibility provided by digital infrastructures, they also encounter infrastructural misalignments, such as unstable connections and opaque algorithmic matching processes. These challenges are addressed through practices of domestication that reconfigure the therapeutic setting, integrating care into mobile and domestic environments. By analysing user practices, the paper investigates how mental health is domesticated through digital platforms and shaped by platform imaginaries, reconfiguring the therapeutic setting as a datafied and everyday space.
4 May 2026
Read appraisal →Molecular psychiatry
A five-year risk prediction model of cardiovascular disease in individuals with bipolar disorder: a nationwide register study from Sweden
Cardiovascular disease (CVD) risk prediction models for the general population may not provide accurate predictions in individuals with bipolar disorder (BD) who have elevated risks of cardiometabolic conditions and premature mortality. Therefore, we aimed to: 1) develop a five-year CVD risk prediction model in this population by using nationwide register data from Sweden, 2) investigate whether the performance improved when we considered additional risk factors, including psychiatric comorbidity, psychotropic medication, and socio-demographic variables, compared to using established CVD risk factors only, and 3) whether machine learning approach provided improvements compared to standard logistic regression models. We followed 33,933 persons with BD aged 30-82 years old, without previous CVD, from the date of BD diagnosis registered between 2007-2014, for up to five years. The logistic regression model containing only established risk factors yielded an area under the receiver operating characteristic curve (AUC) of 0.76 (95% confidence interval 0.74-0.78) in the test dataset, while the logistic regression model and the best performing machine learning model including additional predictors yielded similar results (AUC was 0.77 (0.75, 0.79) in both models). The performance of logistic regression models slightly improved with additional predictors when continuous risk scores were used. In conclusion, standard logistic regression and established CVD risk factors may be sufficient to predict CVD in individuals with BD when using population register-based data from Sweden. External validation across diverse healthcare settings and rigorous assessment of clinical impact will be crucial next steps before implementing these models in clinical practice.
4 May 2026
Read appraisal →International journal of neural systems
Data-related Ablation for Reinforcing Deep Learning in Explaining Complex Phenomena
Deep Learning (DL) models excel at automatically learning intricate patterns within complex data, but their black box nature undermines human trust. To address this, current validation strategies typically focus on the model itself, modifying its architecture to assess the role and importance of the components. However, this model-centric view overlooks the critical learning substrate, which is represented by the data, implicitly assuming that it accurately represents the target phenomenon. This implicit trust in data means that evaluation may fail to detect whether high performance stems from exploiting biases or data quirks rather than learning relevant patterns. We present a novel data-related ablation as a complement to the traditional architectural ablation. Using this framework for Electroencephalography (EEG) signals of Emotional Recognition (ER) and Motor Execution (ME) as a case study, we show that seemingly high-accuracy models often rely heavily on process-irrelevant features, maintaining performance even when key information is eliminated. This shows that a standard, data-independent evaluation can be misleading about whether a model truly captured the intended process; the proposed approach helps distinguish robust learning from leaning on incidental characteristics. Therefore, incorporating data-related ablation is essential for developing reliable and generalizable DL models in fields that rely on data derived from complex and often not completely known phenomena.
4 May 2026
Read appraisal →Journal of medical Internet research
Digital Health Interventions for Family Members of ICU Patients: Current Evidence and Future Directions
A recent systematic review and meta-analysis of randomized trials evaluating digital health interventions for family members of intensive care unit (ICU) patients found no significant improvements in anxiety, depression, posttraumatic stress, quality of life, or communication quality. Rather than concluding that digital approaches are inherently ineffective, we argue that these null findings reflect identifiable and remediable limitations in intervention design, outcome measurement, and trial methodology. In this commentary, we examine four structural barriers that currently constrain the evidence base and outline the conditions that next-generation trials must meet to adequately address the questions raised by this review.
4 May 2026
Read appraisal →PloS one
Interpretable multivariate survival models: Improving predictions for conversion from mild cognitive impairment to Alzheimer's disease via data fusion and machine learning
Accurately predicting which individuals with mild cognitive impairment (MCI) will progress to Alzheimer's disease (AD) can improve patient care. This study examines the role of quantitative MRI (qMRI), cognitive evaluations, apolipoprotein [Formula: see text]4 (APOE [Formula: see text]4), and cerebrospinal fluid (CSF) biomarkers in Cox survival models to predict progression from MCI to AD. Data from 564 participants in the ADNI study, who transitioned from MCI to AD, were analyzed. The data set included 330 features encompassing qMRI, cognitive assessments, CSF biomarkers, and APOE [Formula: see text]4 status. Advanced machine learning (ML) methods were applied to evaluate the importance of these data sources, select relevant features, and develop interpretable Cox survival models within a cross-validation framework. The top optimized model achieved a sensitivity of 0.69, 95% CI [0.63, 0.76], and a specificity of 0.87, 95% CI [0.83, 0.90], and used all data sources. The results demonstrated that combining qMRI features with cognitive assessments, CSF biomarkers, and APOE [Formula: see text]4 status, analyzed using the BSWiMS model, resulted in a substantial improvement in the ability to predict progression from MCI to AD, achieving 81% precision and 87% specificity. These results exceed those obtained with other models evaluated. Finally, biomarker analysis showed that cognitive scores are the most relevant features to predict conversion, followed by CSF and qMRI biomarkers. These findings highlight the value of integrating multiple data sources in highly interpretable Cox survival models for the early identification of individuals at risk for AD.
4 May 2026
Read appraisal →Psychiatry research
A machine learning approach to predicting clinical trajectories in bipolar disorder
BACKGROUND: Bipolar disorder (BD) is characterized by significant heterogeneity in clinical presentation, age of onset, and diagnostic delays. Identifying clinical trajectories and subtypes is a central aim of precision psychiatry, with the goal of improving risk stratification and characterization of illness courses, enabling early recognition of high-risk patients and facilitating timely interventions that improve long-term outcomes. METHODS: This is a dual-site retrospective cross-sectional secondary analysis of 203 clinical datasets from BD patients to develop machine learning models: Random Forest, Weighted Logistic Regression (LR), and XGBoost, supplemented by statistical modelling, to predict age of onset timing (early/late onset) and illness severity. Survival analysis (Kaplan-Meier and Cox proportional hazards) investigated factors influencing diagnostic delay, while clustering identified patient subtypes. RESULTS: Single-split Weighted LR (AUC = 0.733) and nested cross-validation XGBoost (AUC = 0.729) achieved superior performance in predicting onset timing. Cluster analysis identified three distinct phenotypes: baseline low complexity, late-onset familial-severe, and an early-onset comorbid group. Survival analysis revealed that the time to diagnosis was independent of clinical risk profiles (Log-Rank p = 0.34), with the most severe cases experiencing significant diagnostic latency. CONCLUSIONS: Machine learning accurately predicts BD clinical trajectories using routinely collected data in an African cohort. Diagnostic delay did not differ significantly across clinical risk or severity strata, indicating that the measured variables did not explain variation in time to diagnosis.
4 May 2026
Read appraisal →PloS one
Prevalence of perinatal depression in Ethiopia: An umbrella review of systematic review and meta-analysis studies
BACKGROUND: Perinatal depression is a significant public health concern that affects women during pregnancy and the postpartum period. Despite being acknowledged globally, the burden of perinatal depression is particularly profound in low and middle-income countries, such as Ethiopia. This umbrella review is therefore intended to systematically consolidate findings on perinatal depression among Ethiopian women to better understand its prevalence, thereby highlighting the gaps in current research and informing future interventions. METHODS: This umbrella review used the PRIOR checklist for the reviews of systematic review and meta-analytic studies. The review protocol has been registered on PROSPERO: CRD42023495174. PubMed, EMBASE, and PsycINFO databases were searched for the presence of systematic review and meta-analysis studies. The quality of included articles has been evaluated with a measurement tool to assess systematic review and meta-analysis studies (AMSTAR). A novel graphic approach with an estimated corrected covered area (CCA) has been used to determine the degree of overlap of primary studies in the systematic review and meta-analysis studies. The weighted random effect model was used during the meta-analysis. RESULT: A total of 28 unique primary studies and 8 systematic reviews, and meta-analysis studies with 15,592 participants were included in this umbrella review. The pooled prevalence of perinatal depressive symptoms in the included systematic review and meta-analysis studies ranges from 20.1% to 25.8%. The pooled umbrella prevalence of perinatal depressive symptoms among women in Ethiopia was 22.49% (95 CI%:21.38, 23.59). The pooled umbrella analysis revealed that the antenatal and postnatal depressive symptoms were 22.76% (95% CI: 19.9, 25.62) and 21.75% (95% CI: 21.03, 22.48), respectively. In addition, the pooled prevalence of perinatal depression in studies that included 10 or below primary studies is 22.86% (95%CI:20.39, 25.33), and in those that included below 10 primary studies, it was 22.10% (95%CI: 21.55, 22.65). The novel graphic presentation depicted a very high degree of overlap of primary studies in the included systematic reviews and meta-analysis studies; corrected covered area (CCA) of 25.5%. Four of the included studies (fifty percent) had high methodological quality, and the remaining four relied on a moderate quality range. CONCLUSION: The pooled overall, antenatal, and postnatal prevalence of depression symptoms was high in Ethiopia, with no significant difference during the antenatal and postnatal period. An improved understanding of perinatal depression will therefore guide policymakers and health practitioners in developing targeted strategies to alleviate this mental health challenge.
3 May 2026
Read appraisal →PloS one
Enhanced graph attention network by integrating Long Short-Term Memory for artificial emotion representation in multi-modality datasets
Emotion representation is a critical aspect of artificial intelligence, particularly in human-computer interaction and affective computing. Emotion recognition from multi-modal data remains challenging due to the complex semantic relationships between textual, audio, and visual features. This study proposes a hybrid model combining Enhanced Graph Attention Networks and Bidirectional Long Short-Term Memory to address this challenge. First, E-GAT captures structural dependencies between emotional features by constructing a semantic graph from text embeddings. Second, Bi-LSTM models temporal dynamics of sequential data, enabling effective integration of contextual information. We evaluated the model on three benchmark datasets: SemEval-2018 (text-only), RAVDESS (audio-visual), and CMU-MOSEI (multi-modal). Experimental results show that the proposed model achieves state-of-the-art performance: 58.5% accuracy and 68.7% F1-score on SemEval-2018, outperforming baseline models. On multi-modal datasets, it achieves 78.9% accuracy (RAVDESS) and 82.3% accuracy (CMU-MOSEI), demonstrating robust cross-modal generalization. This work advances emotion recognition by providing a unified framework for both text-only and multi-modal scenarios, with applications in human-computer interaction and mental health monitoring.
3 May 2026
Read appraisal →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
Intrinsic regional brain activity differences and its relationship with neurotransmitter and gene expression in postpartum depression: a voxel-based meta-analysis
BACKGROUND: Inconsistent findings persist across resting-state functional imaging studies of regional brain alterations in postpartum depression (PPD), while connections to transcriptional profiles and neurotransmitter systems remain largely uncharacterized. METHODS: We performed a whole-brain voxel-wise meta-analysis of resting-state functional imaging studies comparing PPD patients and healthy controls using SDM-PSI software. JuSpace toolbox analyzed atlas-based nuclear imaging-derived neurotransmitter maps, and transcriptional data were sourced from the Allen Human Brain Atlas. RESULTS: Our systematic review identified 12 functional imaging studies (475 PPD patients, 504 controls). Patients with PPD displayed increased resting-state functional activity in the left inferior occipital gyrus and left precuneus as well as decreased resting-state functional activity in the right amygdala and left precentral gyrus. These functional alterations spatially overlapped with serotonergic, dopaminergic, and VAChT systems. Transcriptional analysis revealed PPD-related gene enrichments in ion channel function (transmembrane transport, gated/passive channels) and channel complexes. CONCLUSIONS: The meta-analysis revealed functional alterations within the DMN, limbic, and primary sensorimotor systems in PPD patients. These changes were linked to neurotransmitter alterations and genetic modulations underlying brain dysfunction. Collectively, these findings advance mechanistic understanding of PPD pathophysiology.
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
Non-pharmacotherapy for craving in methamphetamine use disorder: A systematic review and network meta-analysis of randomised controlled trials
OBJECTIVES: We evaluated the effects of non-pharmacotherapy on quality of life (QOL) and craving in individuals with methamphetamine use disorder (MUD), ranking the efficacy of various protocols. METHODS: We searched multiple databases for randomized controlled trials from inception to Dec 2025. The analysis included 53 studies with 3174 participants, investigating interventions such as exercise, transcranial magnetic stimulation (TMS), transcranial direct current stimulation, behavioral therapy, and acupuncture. The protocol was prospectively registered in PROSPERO (CRD42023443291). RESULTS: For craving reduction, both exercise and TMS demonstrated statistically significant benefits compared to conventional rehabilitation. Combined aerobic/resistance exercise and 10 Hz repetitive TMS targeting the left dorsolateral prefrontal cortex emerged as the most effective specific protocols within their respective categories. Furthermore, preliminary evidence from a limited number of studies indicated that exercise, acupuncture, and behavioral therapy improved patients' QOL. CONCLUSION: This study confirms that several non-pharmacotherapies are effective for managing craving in MUD. Exercise and TMS are particularly promising for reducing craving. The findings, synthesized within a novel "Intervention-Circuit-Symptom" integrative framework, provide a robust evidence base to inform clinical decision-making and guide the future development of MUD treatment guidelines.
1 May 2026
Read appraisal →Behavioural brain research
Influences of high-definition transcranial direct current stimulation (HD-tDCS) and cognitive reappraisal on emotions and aggression following social exclusion
Social exclusion can trigger negative emotions and aggressive behavior, while emotion regulation strategies and neuromodulation techniques may mitigate these effects. This study employed a 2 × 2 factorial design to investigate the impacts of high-definition transcranial direct current stimulation (HD-tDCS) and cognitive reappraisal on emotions and aggression following social exclusion. Thirty-two university students were randomly assigned to four groups: active HD-tDCS + cognitive reappraisal, sham HD-tDCS + cognitive reappraisal, active HD-tDCS + no reappraisal, and sham HD-tDCS + no reappraisal. Results showed that active HD-tDCS significantly reduced aggressive behavior in both the Competitive Reaction Time Task and Job-relevant Evaluations Task, likely by enhancing prefrontal regulatory control. Cognitive reappraisal significantly decreased negative emotions but did not directly reduce aggressive behavior, suggesting a dissociation between emotional and behavioral regulation pathways. No significant interaction was found between HD-tDCS and cognitive reappraisal, possibly due to their independent neural mechanisms. These findings highlight the potential of HD-tDCS and cognitive reappraisal as interventions for social exclusion-induced aggression and emotional dysregulation.
1 May 2026
Read appraisal →Scientific reports
Intelligent virtual agents in psychotherapy: a safety evaluation across high-risk mental health scenarios
The growing burden of mental illness and limited access to evidence-based psychotherapy have increased interest in artificial intelligence (AI)-driven conversational agents as potential supports for mental health care. In this exploratory pilot study, we examined the safety and feasibility of an intelligent virtual agent (IVA) designed to simulate psychotherapeutic interactions, with a focus on high-risk situations involving suicidality and substance use. Two licensed psychotherapists engaged in scripted interactions with the IVA across 12 predefined scenarios addressing suicidality and substance abuse. The IVA was powered by GPT-4omni and embedded in a Unity-based avatar. After each interaction, testers evaluated acceptance, usability, and human-robot interaction. Two independent psychotherapists rated the IVA's responses using a structured scale assessing guideline adherence, risk recognition, help provision, de-escalation, and empathy. No real patients were involved; all interactions were simulated for safety testing purposes. The IVA showed preliminary indications of good usability and generally empathic responses. However, problematic responses occurred in 29% of conversations, with 12.5% rated as highly critical. Responses rated as "critical" or "highly critical" referred to outputs that failed to provide adequate support, showed insufficient risk recognition, or included ethically problematic suggestions. Key concerns included inadequate recognition of risk, normalization of substance use, and insufficient referral to crisis resources, particularly in scenarios involving underage alcohol access and suicide-related inquiries. In this small, expert-based pilot safety evaluation, the findings suggest that although AI-based agents may improve access to mental health support, rigorous safety evaluation, clinical oversight, and robust safeguards are essential prior to clinical deployment. No clinical conclusions can be drawn from this simulated study.
1 May 2026
Read appraisal →The pharmacogenomics journal
The role of cytochrome P450 polymorphisms in the pharmacokinetics of oral esketamine
Oral esketamine displays therapeutic potential for treatment-resistant depression. However, treatment is complicated by a low and variable bioavailability. Genetic polymorphisms in the cytochrome P450 (CYP) enzymes responsible for esketamine's metabolism may influence bioavailability. In a subsample of patients included in a placebo-controlled trial investigating repeated, low dose oral esketamine (N = 18), blood samples were collected four hours after dosing. CYP2B6 516 G > T, CYP3A4*22, and CYP3A5*3 genotyping was performed, and the plasma levels of esketamine and its metabolites were measured. CYP2B6 516 G > T carriers (Mdn = 5.1 µg/L) demonstrated higher esketamine plasma levels compared to non-carriers (Mdn = 2.1 µg/L) (U = 45.00, p = 0.032). No significant associations were observed for CYP3A4*22 carriership (n = 2) or CYP3A5*3 heterozygosity (n = 2), nor for esketamine's metabolites. In summary, CYP2B6 516 G > T is associated with higher esketamine plasma concentrations. Further research with more participants is warranted to draw conclusions about CYP3A4*22 and CYP3A5*3. Clinical trial registration: NTR6161 (Dutch Trial Register).
28 Apr 2026
Read appraisal →Behavior research methods
Lexique 4: A major upgrade of the Lexique French lexical database
Lexique 4, an updated French lexical database, expands upon its predecessor, Lexique 3, by incorporating several significant improvements to enhance its utility in psycholinguistics, computational linguistics, and education. The new version is based on a larger corpus of 316 million words derived from 65,317 documents, including movie, TV show, and documentary subtitles, which offers more accurate frequency estimates and includes contemporary neologisms. Lexique 4 introduces new variables, such as orthographic surface frequency, contextual diversity (CD), and detailed morphological structure, which provide a more comprehensive view of lexical properties. We find that contextual diversity is a slightly better predictor than word frequency, in line with previous work. Moreover, the integration of lexical decision times from the French Lexicon Project into Lexique 4 facilitates more in-depth linguistic research. Enhancements to the user interface, including a redesigned web platform, enable dynamic searches and sorting capabilities, increasing accessibility and usability for researchers. Statistical analyses indicate that the updated frequency measures in Lexique 4 are better predictors of lexical decision times compared to Lexique 3, supporting the value of these enhancements. Overall, Lexique 4 represents a comprehensive and flexible tool for analyzing French lexical properties, making it an essential asset for a broad range of users.
27 Apr 2026
Read appraisal →BMJ open
Breastfeeding initiation and continuation among women on medicines: a systematic review and meta-analysis
OBJECTIVES: Breastfeeding provides essential health benefits for infants and mothers, yet initiation and continuation can be influenced by maternal medication use. Uncertainty regarding medication safety may lead to early discontinuation or avoidance of necessary treatment. We conducted a systematic review and meta-analysis to estimate the prevalence of breastfeeding initiation and continuation at 6 months post partum among women using medications. DESIGN: Systematic review and meta-analysis using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. DATA SOURCES: PubMed, Web of Science, Academic Search Premier (EBSCO) and Google Scholar were searched up to 30 November 2024. ELIGIBILITY CRITERIA: For selecting studies: We included cohort, cross-sectional and observational studies reporting breastfeeding initiation at birth and continuation at 6 months post partum among women aged ≥18 years using medications during pregnancy or post partum. Studies reporting medication type and duration of breastfeeding were prioritised. DATA EXTRACTION AND SYNTHESIS: Two independent reviewers screened, extracted and coded data using standardised methods. Risk of bias was assessed using validated tools. Pooled prevalence estimates were calculated using a random effects meta-analysis and meta-regression was performed to explore factors associated with breastfeeding outcomes. Findings were summarised in GRADE evidence profiles and synthesised qualitatively. RESULTS: Of 286 retrieved articles, 29 met the inclusion criteria and 28 were included in the meta-analysis. 14 (48%) were cohort studies reporting breastfeeding prevalence while using medications. The pooled prevalence of breastfeeding initiation among women on medication during pregnancy or post partum was 75.2% (95% CI 69.8% to 80.7%; range 14-98%), with high heterogeneity (I²=99.2%, p<0.001). Overall prevalence of breastfeeding at 6 months post partum was 53.2% (95% CI 43% to 63%). Meta-regression showed that women in high-income countries were less likely to initiate (β=-0.27, 95% CI -0.54 to -0.00, p=0.04) or continue breastfeeding at 6 months (β=-0.27, 95% CI -0.54 to -0.01, p=0.04). Women using antidepressants (β=-0.45, 95% CI -0.87 to -0.02, p=0.04) and opioids (β=-0.50, 95% CI -0.93 to -0.08, p=0.02) were less likely to continue breastfeeding at 6 months. Women living with HIV on ART in low- and middle-income countries had higher continuation rates, likely due to supportive public health programmes. CONCLUSION: Breastfeeding initiation among women using medications is generally high, but continuation declines substantially, particularly among women on psychotropic, opioid or antiepileptic drugs. HIV status, medication type, healthcare support and national guidelines influence breastfeeding outcomes. Targeted interventions, including provider education, medication-specific lactation guidance and supportive workplace policies, are essential to promote sustained breastfeeding among women using medications.
27 Apr 2026
Read appraisal →PloS one
The stress reduction potential of Bhagavad Gita and Yoga for healthcare workers during the COVID-19 pandemic: A randomized controlled trial
BACKGROUND: Healthcare workers (HCWs) experienced elevated psychological distress during the COVID-19 pandemic. Culturally embedded, non-stigmatizing approaches may improve uptake of mental health support in this population. We aimed to evaluate the impact of Yoga, Bhagavad Gita learning, and their combination (Yoga+Gita) on psychological distress among HCWs. METHODS: We conducted a prospective, single-center, parallel-group randomized controlled trial at a secondary hospital in India. Eligible HCWs (>18 years, without pre-existing diagnosed mental health conditions) were randomized in a 1:1:1:1 ratio to: Yoga (daily 60-min sessions for one week), Gita (daily 60-min teaching of Bhagavad Gita Chapter 2 for one week), Yoga+Gita (30 min Yoga + 30 min Gita daily for one week), or Control (usual care). Psychological distress was assessed using the Generalized Anxiety Disorder-7 (GAD7) scale at baseline (B-GAD7), immediately post-intervention (IPI-GAD7), and 45 days post-intervention (DPI-GAD7). Participants were aware of group assignment, but data analysts were blinded. Group comparisons used Friedman and Kruskal-Wallis tests with Bonferroni-adjusted post-hoc analyses. We hypothesized that (1) all three interventions would reduce GAD-7 scores compared with control, (2) Gita would produce more durable effects than Yoga alone, and (3) the combined Yoga+Gita intervention would yield the greatest overall reduction in psychological distress. FINDINGS: Sixty-eight participants were enrolled (17 per group), with no losses to follow-up. B-GAD7 did not differ across groups (p = 0.908). Compared with Control, Yoga+Gita reduced anxiety immediately (p = 0.009), and Gita reduced anxiety at 45 days (p = 0.047). A clinically meaningful (≥4-point) GAD7 reduction at 45 days occurred in 17.6% (Yoga), 47.1% (Gita), 41.2% (Yoga+Gita), and 11.8% (Control) of the participants. Exploratory analyses suggested larger benefits among women and among those who continued practice. INTERPRETATION: Yoga and Gita-learning, particularly in combination, were associated with reductions in psychological distress among HCWs. TRIAL REGISTRATION: Prospectively registered with the CTRI (CTRI/2021/10/037365).
27 Apr 2026
Read appraisal →PloS one
Protocol for a randomized pilot trial of COMPASS, an open‑source, culturally adapted cognitive behavioral therapy program for forcibly displaced Venezuelan adults in Peru
Task-sharing approaches have shown promise in low-resource settings, yet few culturally adapted interventions have been systematically evaluated for forcibly displaced populations. Since 2016, over 1.7 million Venezuelans have migrated to Peru, facing significant barriers to healthcare and elevated risks of anxiety, depression, and post-traumatic stress disorder (PTSD). This protocol describes COMPASS (Cognitive-behavioral Open-source Mental-health Program Adapted for migrants, Sustainably delivered by lay providers and Supported by evidence). COMPASS is a transdiagnostic, open-source cognitive behavioral therapy program co-designed with forcibly displaced populations. This protocol describes the procedures for an ongoing randomized pilot trial with n = 90 forcibly displaced Venezuelan people (Clinicaltrials.gov: NCT06635486). COMPASS guides, or lay providers, trained through an intensive apprenticeship model, will deliver 6-12 weekly remote sessions. Primary outcomes include changes in anxiety, depression, and PTSD symptoms, assessed with validated Spanish-language measures. Secondary outcomes include feasibility (recruitment, retention, fidelity) and acceptability (therapist and participant ratings). Exploratory outcomes will examine integration, migration experiences, and demographic moderators of intervention effectiveness. Analyses will follow the intention-to-treat principle, using descriptive statistics and regression models to evaluate symptom trajectories across baseline, post-intervention, and 3- and 6-month follow-ups. This study represents the first effectiveness evaluation of an open-source, lay-delivered CBT program tailored for forcibly displaced people in Peru. Findings will inform feasibility, acceptability, and preliminary effectiveness of COMPASS, with potential to expand scalable, culturally relevant mental health services for forcibly displaced populations in resource-constrained settings worldwide.
27 Apr 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 →Journal of obstetrics and gynaecology : the journal of the Institute of Obstetrics and Gynaecology
Antenatal psychological interventions may ameliorate postpartum depression: a systematic review and meta-analysis
BACKGROUND: This study aimed to systematically evaluate the effects of psychological interventions during pregnancy on the intervention outcomes and incidence rate in women with postpartum depression. METHODS: Databases (PubMed, Medline, Web of Science, Cochrane Library, EMBASE) were searched from inception to December 2024 for randomised controlled trials assessing prenatal psychological interventions (e.g., mindfulness, psychosocial support) in pregnant women without severe mental illness. Primary outcomes included Edinburgh Postnatal Depression Scale (EPDS), Patient Health Questionnaire-9 (PHQ-9), Perceived Stress Scale-10 (PSS-10), Spielberger State-Trait Anxiety Inventory (STAI) scores, and PPD incidence within 6 weeks postpartum. Quality was assessed using the Cochrane risk-of-bias tool; meta-analysis was performed with RevMan 5.3. RESULTS: A total of 10 articles were included, with 6700 study subjects in total, including 3345 in the intervention group and 3355 in the control group, all of which were randomised controlled trials. The incidence rate of postpartum depression in the intervention group was significantly reduced (OR = 0.73, 95% CI [0.57-0.95], P = 0.02). The scores of EPDS, PHQ-9, PSS-10, and STAI in the intervention group were all lower than those in the control group. CONCLUSION: Psychological interventions during pregnancy can significantly reduce postpartum depression-related scale scores and incidence rates, and improve depressive symptoms, anxiety, and perceived stress. However, due to the heterogeneity of intervention types, this study cannot determine the optimal intervention method and thus provides preliminary evidence for clinical prevention. Clinicians should select interventions based on resource availability and individual characteristics of pregnant women, while future studies should focus on direct comparisons of different intervention types to provide more targeted guidance. Postpartum depression is a common mental health problem that affects many women after childbirth. It can harm both the mother’s well-being and the baby’s development. This study combined data from ten clinical trials, including 6,700 participants, to evaluate whether psychological interventions during pregnancy can help prevent postpartum depression. The results showed that women who received psychological support—such as mindfulness training, counselling, or group therapy—had fewer depressive symptoms, lower anxiety and stress levels, and a significantly reduced incidence rate compared with those who received routine care. These findings suggest that providing psychological care during pregnancy is an effective way to reduce the risk of postpartum depression and to promote mental health for both mothers and infants. Early psychological support should be considered an important part of prenatal care.
26 Apr 2026
Read appraisal →JMIR mental health
Effectiveness of a Fully Automated Mobile Therapeutic Versus a General Chatbot in Reducing Depression and Anxiety and Improving Well-Being: Feasibility Randomized Controlled Trial
BACKGROUND: Given the increasing prevalence of depression and anxiety disorders and enduring barriers to care, there is a critical need for alternative treatment options. Generative artificial intelligence (AI) chatbots show promise for increasing access to mental health care, though more direct research is needed to establish their efficacy. OBJECTIVE: This pilot study aimed to test the efficacy of a generative mental health chatbot rooted in solution-focused therapy compared to the general-purpose ChatGPT and an assessment-only control (AOC) group on depression, anxiety, and well-being. METHODS: A total of 185 English-speaking adults were recruited online and randomly assigned to one of three groups: AI therapy, ChatGPT, or AOC. Of these, 147 eligible participants filled out a pretreatment assessment. Over a 3-week period, the AI therapy group (n=44) was instructed to complete 3 structured, fully automated app-based sessions per week (9 total), while the ChatGPT group (n=60) was instructed to engage in 9 unstructured conversations with ChatGPT (GPT-4o-based models). The control group (n=43) received no intervention. In the AI therapy group, 39% (n=17) completed all sessions, as did 62% (n=38) of those in the ChatGPT group. Primary outcome measures, self-assessed online at baseline and postintervention, included the Patient Health Questionnaire-9 (PHQ-9), Overall Depression Severity and Impairment Scale (ODSIS) (depression), 7-item Generalized Anxiety Disorder Scale (anxiety), and World Health Organization Well-Being Index (5-item version) (well-being). Linear mixed effects models were used for data analysis. RESULTS: Compared to AOC, both the AI therapy group (d=-0.47; P=.01) and the ChatGPT group (d=-0.44; P=.02) demonstrated significant reductions in depression scores measured by PHQ-9. The AI therapy group showed nonsignificant reductions in anxiety (d=-0.37; P=.11) and ODSIS depression scores (d=-0.25; P=.22) and an increase in well-being (d=0.12; P=.53) compared to AOC. Similarly, a nonsignificant reduction in anxiety (d=-0.27; P=.22) and ODSIS depression scores (d=-0.12; P=.53) and an increase in well-being (d=0.20; P=.29) were observed in the ChatGPT group compared to AOC. The AI therapy group did not significantly outperform the ChatGPT group on any outcomes (PHQ-9: b=-0.19; d=0.03; P=.87; 7-item Generalized Anxiety Disorder Scale: b=-0.57; d=-0.11; P=.62; ODSIS: b=-0.59; d=-0.13; P=.50; and WHO: b=-0.38; d=-0.07; P=.69). CONCLUSIONS: Both the structured generative AI chatbot and ChatGPT showed a significant reduction in depression scores compared to the control group. No significant effects were observed across other outcomes, although descriptive trends indicated improvements in anxiety. While the AI therapy group showed descriptively better outcomes for depression and anxiety, differences between groups were not significant. A larger sample and longer intervention may be needed for the emerging trends to yield clinically meaningful effect sizes.
26 Apr 2026
Read appraisal →JMIR mental health
Expected Competencies and Personal Attributes of Digital Health Navigators to Support Digital Mental Health Care: Focus Group and Interview Study With Patients and Health Care Professionals
BACKGROUND: Digital mental health apps (DMHAs), and in particular digital therapeutics (DTx), offer promising opportunities to support mental health care. However, their effective use in outpatient settings in Germany remains limited. To overcome this gap, the role of digital health navigators (DHNs) has been introduced. DHNs are trained individuals who support patients and health care professionals in selecting, using, and integrating DMHAs into care. Despite increasing interest in this role, there is limited evidence on the competencies, knowledge, and personal attributes required for DHNs to work effectively in mental health settings. OBJECTIVE: The study aims to explore the expected competencies, knowledge areas, and personal attributes that DHNs need to effectively support the implementation and use of DTx in outpatient mental health care. METHODS: As part of the prestudy of the Digital Navigators for Acceptance and Competence Development with Mental Health Apps (DigiNavi) study, a qualitative study was conducted involving 35 participants (7 general practitioners, 8 patients in general practice, 11 outpatient psychiatrists/psychologists, and 9 patients in psychiatric outpatient clinics) from different general practices and psychiatric outpatient clinics in Germany. A total of 17 semistructured interviews and 4 focus groups were conducted to explore expectations of DHNs. Data were analyzed using qualitative content analysis. RESULTS: Participants emphasized that DHNs should combine strong interpersonal skills (empathy, patience, and sensitive communication) with technical and basic clinical competencies. Most favored DHNs as integrated clinical team members (eg, medical assistants), citing their existing patient relationships, but noted time and training constraints. Key expectations included the ability to support patients with DTx use, adapt communication to individual needs, and convey data privacy information clearly. Foundational knowledge of mental health conditions and sensitivity to crises were considered important for identifying warning signs and escalating concerns. While DHNs were seen as essential intermediaries between patients, health care professionals, and DTx, participants highlighted the necessity for clearly defined roles, structured training, and realistic expectations to prevent role overload and enable sustainable implementation in outpatient mental health care. CONCLUSIONS: DHNs require a specialized skill set that bridges clinical understanding, digital expertise, and interpersonal competence. Our results lay the groundwork for developing training curricula and implementation strategies that align with real-world expectations for the DHN role. Defining these core competencies is essential for supporting the sustainable and effective integration of DMHAs into mental health care.
26 Apr 2026
Read appraisal →Archives of orthopaedic and trauma surgery
Analysis of mental health outcomes in major versus minor upper extremity amputations: a retrospective national database study
BACKGROUND: Upper extremity amputations are associated with developing psychiatric conditions. The aim of this study is to identify differences in mental health outcomes for patients undergoing major versus minor upper extremity amputations. METHODS: Data were obtained from the PearlDiver database between 2010 and 2022 using Current Procedural Terminology and International Classification of Diseases codes. Patients aged 10 and above without prior mental health diagnoses or antidepressant prescription records who underwent upper extremity amputations were stratified by major (shoulder disarticulation, arm, forearm, wrist, transmetacarpal) versus minor (single metacarpal, digit, phalanx) amputations. Demographic characteristics and rates of mental health diagnoses, antidepressant prescriptions, and psychotherapy care were assessed for 90 days and one year postoperatively, using Welch's T-tests and Pearson's chi-squared tests. RESULTS: Patients with major amputations had increased risk of mental health diagnoses at 90 days (OR: 3.29, p < 0.001) and at one year (OR: 2.01, p < 0.001). Both groups had higher rates of mental health diagnoses than the general population. Patients undergoing major amputations had higher odds of starting antidepressants at 90 days (OR 3.82, p < 0.001) and at one year (OR 2.38, p < 0.001). Psychotherapy care was significantly increased after major amputations at 90 days (OR 5.47, p < 0.001) and at one year (OR 4.18, p < 0.001). CONCLUSIONS: Mental health disorders, antidepressant use, and psychotherapy care are significantly higher for major upper extremity amputations compared to minor amputations. Surgical teams should provide mental health resources to mitigate negative effects from mental health needs after upper extremity amputation. LEVEL OF EVIDENCE: III.
26 Apr 2026
Read appraisal →PloS one
The journey to diagnosis and care of functional neurological disorder (FND).
PURPOSE: Functional neurological disorder (FND) is a common condition, associated with high disability and healthcare costs, and poor treatment access. This qualitative study aimed to explore participants' experiences of being diagnosed with functional neurological disorder (FND), accessing treatment, and navigating life after diagnosis. Participants were drawn from a randomised feasibility study of eye movement desensitisation and reprocessing therapy for people with FND (MODIFI, Trial Registration: NCT05455450 (www.clinicaltrials.gov)). METHODS: Reflexive thematic analysis was used to analyse the data from eighteen semi-structured interviews with participants diagnosed with FND. RESULTS: Six main themes were identified: (1) the process of seeking a diagnosis and initial relief from the physical struggles, (2) receiving a diagnosis of FND, (3) treatment for FND, (4) the burden of FND in day-to-day life, (5) the need to assert agency and return to normal, and (6) hopes for the future. CONCLUSION: The findings emphasize the need for personalised and compassionate care for individuals suffering from FND, underpinned by increased service provision within the healthcare system.
25 Apr 2026
Read appraisal →BMC psychiatry
The cross-sectional relationship between body dysmorphic disorder and perfectionism: a meta-analysis
Perfectionism has been theoretically implicated in the development and maintenance of body dysmorphic disorder (BDD), but a quantitative synthesis of its cross-sectional relationship with BDD symptom severity is lacking. The present meta-analysis therefore examines the strength and consistency of this relationship across existing studies. A random-effects meta-analysis of 34 studies comprising a total of 13 107 participants was conducted. Studies were included if they assessed BDD symptoms and perfectionism using validated instruments. Subgroup analyses were used to examine perfectionism dimensions, sample characteristics, and diagnostic subtypes. Partial correlations controlling for depression were examined. The results revealed a moderate and statistically significant correlation between BDD symptoms and perfectionism (r = .32, 95% CI [0.28, 0.36]). The association remained stable across age groups, sample types, and genders, and was comparable in individuals with muscle dysmorphia (r = .34). A separate meta-analysis focusing on individuals with elevated BDD symptoms yielded a comparable effect size (r = .35, 95% CI [0.22, 0.47]). Controlling for depressive symptoms attenuated the relationship, indicating that part of the association may be attributable to shared depressive symptomatology. Significant heterogeneity (I² = 81%) and evidence of publication bias were detected, although the findings were comparable across several sensitivity analyses. The results highlight the relevance of both perfectionistic concerns and perfectionistic strivings as correlates of BDD-related distress and support its consideration within transdiagnostic models. The protocol for this meta-analysis was pre-registered on PROSPERO (Preregistration ID: CRD42024551099).
25 Apr 2026
Read appraisal →Cell reports. Medicine
Bed nucleus of the stria terminalis-nucleus accumbens stimulation for depression: A randomized, double-blind, crossover trial
Although deep brain stimulation (DBS) shows potential in treatment-resistant depression (TRD), the efficacy remains controversial. To assess the efficacy of DBS of the bed nucleus of the stria terminalis (BNST) and nucleus accumbens (NAc) for TRD, we conduct a randomized, double-blind, crossover trial after an open-label optimization stage. Stimulation leads to a mean decrease in the Hamilton Depression Scale-17 (HAMD) scores of 10.1 points (p < 0.001) with a 50% response rate at the end of the open-label stage. Anxiety, quality of life, and disability are also improved. The HAMD scores are significantly lower in patients during active DBS than during sham DBS (p < 0.001). Serious adverse events include suicide (1 patient) and seizure (1 patients). With individual stimulation analysis, we identify optimal stimulation sites, fiber tracts, and functional networks (false discovery rate [FDR] p < 0.05). This study demonstrates the efficacy and safety of BNST-NAc DBS for TRD and reveals optimal brain targets for stimulation. This study was registered at ClinicalTrials.gov (NCT04530942).
25 Apr 2026
Read appraisal →BMJ open
Course of psychological symptoms and the initial management strategies in general practice using electronic health records in the Netherlands
BACKGROUND: Patients who consult their GP about psychological complaints, such as feeling anxious or depressed, are often initially given a psychological symptom diagnosis. However, it remains unclear whether these symptoms will develop into psychiatric conditions, which is crucial for informing patients about their prognosis and guiding GPs in their management. AIMS: To explore the course of psychological symptom diagnoses and compare GPs' management strategies for (a) psychological symptom diagnoses that persisted for more than a year and (b) those that changed into psychiatric conditions during the first year of care. METHODS: We performed a retrospective cohort study using the Family-Medicine Network database. We included all episodes of care (EoC) that started with a psychological symptom diagnosis between 2008 and 2021. We performed negative binomial analyses and logistic regression analyses to compare management strategies and number of contacts between EoC that changed into psychiatric conditions and persistent psychological symptoms (>1 year) during the first year of care. RESULTS: Out of the 14 633 EoC that started with a psychological symptom diagnosis, 79.4% resolved within 1 year, 12.8% persisted as psychological symptoms and 7.8% changed into psychiatric conditions. In EoC that changed into psychiatric conditions, as compared with EoC that persisted as psychological symptoms, we observed a significantly higher number of contacts with the GP (RR=1.76, 95% CI 1.63 to 1.91) as well as an increased total number of interventions (RR=1.71, 95% CI 1.58 to 1.84). CONCLUSION: Most psychological symptoms remain for only a short period of time and only a few persist or change into psychiatric conditions. Future research should investigate the factors that influence patients' decisions to seek further help from their GPs as well as those that contribute to the transition from psychological symptom diagnoses to a psychiatric condition.
25 Apr 2026
Read appraisal →JMIR formative research
Access to Technology-Mediated Community Mental Health Care Among Low-Socioeconomic Status Consumers With Serious Mental Illness: Qualitative Study
BACKGROUND: Access to mental health care is critical for the effective management of serious mental illness (SMI), but consumers with low socioeconomic status (SES) have lower rates of service usage and worse retention in care. Digital technologies are often lauded as a way to bridge access gaps; however, little is known about how technology-mediated care may influence care access among low-SES consumers and how consumers use technology in care access. OBJECTIVE: This study aimed to examine the applicability of Levesque et al's access framework to technology-mediated care for SMI and analyze how low-SES consumers use technology to facilitate care access. Furthermore, the study assesses whether and how technologies are involved in care access at multiple points within the process of accessing care. METHODS: This study used 2 qualitative methods: ethnographic observations at a mental health treatment court and interviews with low-SES consumers with SMI using community mental health care (n=14) and key informant interviews with health and service providers working with this population (n=14). Observations occurred from July 2022 through September 2023, and interviews occurred between January 2022 and May 2024. Data analysis involved both inductive and deductive coding approaches. Data from both the interviews and observations were analyzed in NVivo and further triangulated through analytic memos. RESULTS: Levesque et al's framework required several extensions to accommodate technology-mediated care related to SMI for low-SES consumers: (1) a cyclical rather than linear trajectory; (2) simultaneous care acquisition from multiple health and service providers; (3) staying in care long-term; (4) identification of both one-time and ongoing health needs; and (5) an emergency pathway for entering care. Consumers often faced challenges related to the varied digital requirements of each provider and a dearth of integrative, patient-facing tools like portals. Within this context, some consumers use mobile apps, communication, and telehealth technologies across various care access stages. Consumers used technology by figuring out how to navigate technology-mediated care, especially by leaning on others, such as case managers, for support. These others provided consumers with temporary technologies, showed them how to use technologies, and accompanied them through the process of using technology for accessing care. CONCLUSIONS: This study highlights that accessing care is iterative and ongoing, involving multiple forms of co-occurring service provision. A theoretical contribution of this work is its extension of Levesque et al's care access framework to better reflect technology-mediated care for SMI among low-SES consumers. This work also underscores ongoing challenges for accessing technology-mediated care and the importance of human support in addressing access difficulties. Clinical implications include incorporating digital readiness assessments and providing comprehensive guidance on how consumers can effectively use technologies for care. Future work should investigate how technology-mediated care can make care access easier rather than harder.
24 Apr 2026
Read appraisal →Brain imaging and behavior
Meta-analysis and meta-regression of diagnostic test accuracy of connectome-based predictive modeling in OCD
Functional magnetic resonance imaging studies have reported disruptions in functional connectivity within brain networks, known as connectomes. Researchers have tested connectomes to see whether they serve as biomarkers for various psychiatric conditions. This meta-analysis aims to evaluate the diagnostic test accuracy of predictive models of connectomes derived from resting-state fMRI in diagnosing obsessive-compulsive disorder. A systematic review and meta-analysis were conducted on previous studies assessing the sensitivity, specificity, and accuracy of connectome-based diagnostic models in obsessive-compulsive disorder and healthy controls. Eight studies were identified, comprising 563 individuals with obsessive-compulsive disorder and 564 healthy controls. The results revealed robust diagnostic performance with a pooled sensitivity of 0.827 (95% CI: 0.779-0.867) and specificity of 0.794 (95% CI: 0.759-0.826). Connectome-based diagnostic models demonstrated excellent clinical utility, with an area under the curve of 87% (95% CI: 84%-90%), and significant predictive power as indicated by positive (4.15) and negative (0.21) likelihood ratios, as well as strong diagnostic odds ratio of 18.69 (95% CI: 11.84-29.49). The results highlight the potential of functional connectome-based predictive modeling as a robust tool for accurately diagnosing obsessive-compulsive disorder, with possibility of future implications for early diagnosis, monitoring treatment-related changes, involving in decision modeling, and understanding the biological mechanisms underlying obsessive-compulsive disorder.
20 Apr 2026
Read appraisal →Medicine
Drug-induced neutropenia: A comprehensive pharmacovigilance study of the FAERS database.
Drug-induced neutropenia (DIN) deserves both clinicians' and researchers' attention for the serious clinical consequences. This study aimed to conduct an exploratory analysis of DIN reports in the United States Food and Drug Administration Adverse Event Reporting System database to identify drugs associated with neutropenia. A disproportionality analysis of DIN was conducted using reports submitted to the United States Food and Drug Administration Adverse Event Reporting System from the first quarter of 2004 to the fourth quarter of 2024. Both the reporting odds ratio algorithms and the information component were applied to identify an association between target drugs and DIN events. A total of 223,736 DIN cases were found, and 254 drugs were identified as potentially associated with neutropenia. The top-ranked drug categories were "monoclonal antibodies and antibody-drug conjugates," "other antineoplastic agents," "protein kinase inhibitors," "direct-acting antivirals," "antimetabolites," "alkylating agents," and "immunosuppressants." The top 5 drugs ranked by number of cases were clozapine, lenalidomide, rituximab, palbociclib, and carboplatin. The top 5 drugs ranked by reporting odds ratio signal strength were calcium levofolinate, vinorelbine, gemtuzumab ozogamicin, fludarabine, and daunorubicin. This comprehensive pharmacovigilance study identified drugs potentially associated with neutropenia, which can help clinicians and researchers further understand DIN.
20 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
Drug-associated insomnia: A pharmacovigilance study based on FDA adverse event reporting system.
Insomnia has become an increasingly serious public health issue with complex causes, among which medications act as a significant factor. This study aims to systematically detect and evaluate drug adverse event signals associated with insomnia risk using the Food and Drug Administration Adverse Event Reporting System (FAERS) database. The analysis utilized data from the FAERS database covering January 2004 to December 2024. Disproportionality analysis was conducted using 4 algorithms: reporting odds ratio, proportional reporting ratio, information component (IC), and empirical Bayes geometric mean. Potential risk signals were deemed significant only when all 4 algorithms simultaneously met their thresholds. Subgroup analyses were further performed, stratified by age and sex, to assess the robustness of signals across different populations. From 2004 to 2024, there were 179,697 adverse event reports of insomnia in FAERS in which one or more medications were designated as the primary suspect. The top 30 medications with the strongest signal strength were predominantly nervous system medicines (18 types, 60%), followed by respiratory system medicines (3 types, 10%), and genitourinary system and sex hormones (3 types, 10%). The top 3 medications with the highest reporting frequency were mefloquine, viloxazine, and flibanserin. Subgroup analyses revealed distinct drug signal profiles across age groups and genders, with pediatric cases dominated by nervous system and anti-infective agents, adults and the elderly showing additional endocrine or hormonal signals, and sex specific signals such as finasteride in males and flibanserin in females. This pharmacovigilance study identifies insomnia risk signals across multiple drug classes, underscoring the need for clinical vigilance regarding drug-related sleep disturbances. Further prospective research is required to confirm these associations.
20 Apr 2026
Read appraisal →BMJ open
Implementation of a digital tool for monitoring and managing the emotional and cognitive sequelae of post-intensive care syndrome: ICURA study protocol for a randomised clinical trial
INTRODUCTION: Intensive care units (ICUs) can be a particularly challenging environment for patients who are mentally vulnerable. In addition to the physical stress associated with critical illness and its management, there are physiological and psychosocial factors that can negatively impact a patient's mental health. Approximately half of ICU survivors will experience post-intensive care syndrome, a set of emotional, neuropsychological and physical sequelae that can significantly affect patients' functionality and quality of life, both in the short and long term. The main objective of this study is to investigate whether the ICU Recovery Answers (ICURA) digital follow-up platform can effectively detect emotional and cognitive problems in critically ill patients and its impact on functionality and health-related quality of life during the first year after ICU discharge. METHODS AND ANALYSIS: Multicentre longitudinal prospective study involving ICU adult patients, with randomised follow-up comparing a telemedicine monitoring programme versus usual medical care during 1 year after discharge. A total of 360 participants will be recruited during their ICU admission in two hospitals in Spain. Efficacy outcomes will focus on participants' level of functioning, assessed with the WHO Short Disability Assessment Schedule, and quality of life, measured with the 12-Item Short Form Survey at 1, 6 and 12 months after ICU discharge. Emotional state and cognitive impairment will be evaluated using the Patient Health Questionnaire-9, Generalised Anxiety Disorder-7 and Treatment-Outcome Post-Traumatic Stress Disorder Scale and the Montreal Cognitive Assessment by telephone at 1, 3, 6, 9 and 12 months after ICU discharge. ETHICS AND DISSEMINATION: The implementation of this project is expected to have a direct impact on the satisfaction of ICU survivors, improving their well-being, personalised follow-up and quality of life. Results from this study will be disseminated at various scientific conferences, national and international meetings, and will be shared with the general public and other relevant parties. The dissemination of these results will occur through scientific publications, allowing the medical and scientific community to benefit from the study's findings. Ethics approval from the Ethics Board of Parc Taulí Foundation and Balearic Islands with reference numbers 2022/3031 and IB 5072/22 PI: Protocol version 1 of 18 November 2022. TRIAL REGISTRATION NUMBER: NCT06504979.
20 Apr 2026
Read appraisal →Journal of neural engineering
Physiologically inspired modeling of cortical dynamics through spiking neural networks
Objective. The characterization of neural activity underlying neurophysiological function presents a major challenge in computational neuroscience. Several methods have been proposed to investigate cortical network dynamics by reconstructing underlying neural activity from electroencephalography (EEG) signals. However, these methods generally pose significant mathematical challenges.Approach. This study introduces a novel framework to model the underlying brain activity network from a functional and physiologically-inspired perspective, combining spiking neural networks with EEG signal analysis. The dynamics of single neurons are described by the well-known Izhikevich model, and distinct populations of cortical inhibitory and excitatory neurons are employed to model experimental EEG recordings. Functional interactions among distinct populations are mathematically formalized through connective probabilities.Main results. The proposed framework is validated by testing it on synthetic data, as well as on two experimental datasets comprising data from 30 healthy subjects undergoing a cold-pressure test (CPT), and 36 subjects undergoing a mental arithmetic stressor. Experimental results suggest that the proposed framework provides novel and complementary insights into characterizing neuronal changes in comparison to standard EEG power analysis.Significance. The proposed framework constitutes a promising tool for functionally characterizing the underlying cortical dynamics under pathophysiological conditions.
20 Apr 2026
Read appraisal →PLoS computational biology
'Backpropagation and the brain' realized in cortical error neuron microcircuits
Neural responses to mismatches between expected and actual stimuli have been widely reported across different species. How does the brain use such error signals for learning? While global error signals can be useful, their ability to learn complex computation at the scale observed in the brain is lacking. In comparison, more local, neuron-specific error signals enable superior performance, but their computation and propagation remain unclear. Motivated by the breakthrough of deep learning, this has inspired the 'backpropagation and the brain' hypothesis, i.e., that the brain implements a form of the error backpropagation algorithm. In this work, we introduce a biologically motivated, multi-area cortical microcircuit model, implementing error backpropagation under consideration of recent physiological evidence. We model populations of cortical pyramidal cells acting as representation and error neurons, with bio-plausible local and inter-area connectivity, guided by experimental observations of connectivity of the primate visual cortex. In our model, all information transfer is biologically motivated, inference and learning occur without phases, and network dynamics demonstrably approximate those of error backpropagation. We show the capabilities of our model on a wide range of benchmarks, and compare to other models, such as dendritic hierarchical predictive coding. In particular, our model addresses shortcomings of other theories in terms of scalability to many cortical areas. Finally, we make concrete predictions, which differentiate it from other theories, and which can be tested experimentally.
19 Apr 2026
Read appraisal →Pharmaceutical biology
Valeriana species and insomnia: multi-organ mechanisms and translational perspectives.
CONTEXT: Insomnia is a common sleep disorder that substantially impairs quality of life. Conventional treatments, including cognitive behavioral therapy and pharmacological agents, are often limited by side effects and insufficient long-term safety data. Valeriana species, such as Valeriana officinalis L. and Valeriana jatamansi Jones, have been traditionally used for their sedative and neuroprotective properties. OBJECTIVE: This review evaluates the therapeutic potential of Valeriana species for insomnia, emphasizing their pharmacological mechanisms, multi-organ effects, and evidence from preclinical and clinical studies. METHODS: A systematic literature search was performed in databases including PubMed and Web of Science. Studies addressing Valeriana-induced sleep regulation, neurotransmitter modulation, oxidative stress reduction, and neural, hepatic, and gastrointestinal interactions were included to assess efficacy and safety. RESULTS: Valeriana species modulate neurotransmission, mitigate oxidative stress, and influence hepatic and gastrointestinal functions. Preclinical studies support their sedative and neuroprotective effects, while clinical outcomes vary with formulation, dosage, and individual response. Overall, safety profiles are favorable, though inconsistencies remain. CONCLUSION: Valeriana species represent a promising multi-target strategy for insomnia, integrating neural and peripheral pathways. Future research should clarify the mechanisms underlying multi-organ effects, optimize formulations, and conduct rigorous clinical trials to establish standardized, effective, and safe interventions.
19 Apr 2026
Read appraisal →Journal of health organization and management
Organisational professional support measures to enhance mental health and wellbeing in female healthcare workers: an umbrella review
PURPOSE: This study aims to synthesise evidence on organisational professional support measures that enhance female healthcare workers' mental health and wellbeing. Female healthcare workers face high rates of mental health challenges, impacting their ability to provide optimal care. DESIGN/METHODOLOGY/APPROACH: This umbrella review adhered to the nine-step pathway to conducting umbrella reviews by Cant et al. (2022) and reported following the Preferred Reporting Items for Overview of Reviews checklist (Pollock, 2019). A search of Embase, Medline, CINAHL via EBSCO and the Cochrane Library for English language papers from January 2011 onwards was conducted (search date: February 2024). Following an identification of relevant papers and data extraction, themes were developed using a narrative synthesis approach. FINDINGS: In total, 32 systematic reviews met the inclusion criteria. From the data synthesis, four themes were developed, namely lack of studies specific to female healthcare workers, diversity and variability of interventions, interventions best related to staff outcomes versus organisational outcomes and social connections at work can be supportive or stressful. ORIGINALITY/VALUE: Evidence supports that person-directed interventions including cognitive-behavioural therapy and relaxation measures may enhance staff-level outcomes. In contrast, organisation-directed interventions such as clinical supervision and mentoring, or a combination of person- and organisation-centred interventions, appear to enhance both staff and organisational outcomes. Limitations in existing studies highlight the need for improved research conduct and reporting.
18 Apr 2026
Read appraisal →PloS one
High-frequency vs. low-frequency MIDI-assisted group music therapy in psychiatric inpatients: A randomized controlled trial
BACKGROUND: Music therapy has been increasingly incorporated into psychiatric inpatient care as an adjunctive intervention; however, evidence regarding its comparative effectiveness, particularly with respect to intervention intensity during brief psychiatric hospitalizations, remains limited. Most randomized trials have evaluated interventions delivered over several weeks, leaving uncertainty about the potential differential effects of higher versus lower intervention intensity within short inpatient stays. METHODS: This randomized controlled trial with a parallel-group design was conducted in an adult psychiatric inpatient unit in Bogotá, Colombia. Participants were randomly assigned (1:1) to either a high-frequency music therapy group, which received five sessions delivered on consecutive days within a single inpatient week, or a low-frequency group, which received one session during the same period. Allocation concealment was ensured through computer-generated randomization performed by an independent researcher not involved in participant recruitment or outcome assessment. Primary outcomes were symptoms of depression, anxiety, and stress, assessed using the Depression Anxiety Stress Scale-21 (DASS-21) at baseline and at the end of the intervention week. Secondary outcomes included emotional responses to music therapy, assessed using selected items from the Questionnaire of the Impact of Music Therapy Sessions in Adults (CISMA instrument), and global life satisfaction, measured with the Satisfaction With Life Scale (SWLS). Between-group changes over time were analyzed using repeated-measures mixed-effects linear models. RESULTS: A total of 91 participants were randomized, of whom 74 (37 per group) completed post-intervention assessments and were included in the per-protocol analysis. Between-group analyses revealed a statistically significant group × time interaction for the DASS-21 stress subscale only. Participants in the high-frequency music therapy group showed a greater reduction in stress scores compared with the low-frequency group, with a statistical between-group difference (6.49 ± 5.55 vs. 7.03 ± 5.81; p = 0.023). No statistically significant between-group differences were observed for the DASS-21 anxiety subscale (p = 0.339) or the depression subscale (p = 0.270). Global life satisfaction, as measured by the SWLS, did not differ significantly between groups (p > 0.05). For secondary outcomes assessed using selected CISMA items related to emotional change, between-group differences were small and not statistically significant across all items. Overall, except for stress symptoms, outcomes did not differ significantly between the high-frequency and low-frequency intervention groups. CONCLUSIONS: In this randomized controlled trial, a higher frequency of music therapy sessions was associated with a greater reduction in stress symptoms compared with a lower-frequency intervention during a brief psychiatric hospitalization. However, no between-group differences were observed for anxiety, depression, life satisfaction, or emotional response measures. Overall, these findings indicate limited evidence of differential effectiveness by intervention intensity and should be interpreted cautiously in light of the study's methodological limitations. TRIAL REGISTRATION: ISRCTN registry: ISRCTN87861817 (https://www.isrctn.com/ISRCTN87861817).
18 Apr 2026
Read appraisal →PloS one
EAC-Agent: A deep learning framework for multimodal emotion-aware conversational agent with contextual response generation
Conversational agents, commonly referred to as chatbots, have become an integral part of various important applications, such as customer support and virtual assistants in enterprise-level solutions. Despite their widespread use, current solutions predominantly rely on a single modality input, primarily text, which limits their ability to fully understand the correct emotions of users and respond accordingly. The increasing demand for multimodal inputs in conversation such as text, audio, and videos, highlights the need for a more comprehensive approach. Existing conversational agents face significant challenges in generating emotionally aware responses, as they lack the ability to effectively handle emotion embeddings, leading to limitations in emotional accuracy and contextual appropriateness. Focusing on these challenges, this research work leads to a novel multimodal approach which incorporates features from text, audio, and visual. The EAC-Agent proposed a sequence-to-sequence model with transformer, along with pre-trained embeddings such as GloVe. The self and cross-modal attention on text, audio and visual is used to generate a more emotionally intelligent response. EAC-Agent is validated through comparison with existing techniques on two benchmark datasets. The obtained results demonstrate superior performance in emotion classification and response generation. The proposed model achieves an accuracy of 76.27% on IEMOCAP and 67.57% on MELD for emotion recognition from multimodal user inputs. In addition, the emotion-aware response generation module shows clear improvements, with perplexity values of 39.01 and 42.30, BLEU scores of 0.31 and 0.30, and ROUGE-L scores of 0.45 and 0.44 on IEMOCAP and MELD, respectively. EAC-Agent demonstrates clear superiority over existing models and holds great promise for applications in customer service, healthcare, and other areas requiring empathetic and contextually appropriate interactions.
18 Apr 2026
Read appraisal →PloS one
Statistically valid explainable black-box machine learning: applications in sex classification across species using brain imaging
Sex classification using neuroimaging data has the potential to revolutionize personalized diagnostics by revealing subtle structural brain differences that underlie sex-specific disease risks. Despite the promise of machine learning, traditional methods often fall short in providing both high classification accuracy and interpretable, statistically validated feature importance scores for high-dimensional imaging data. This gap is particularly evident when conventional techniques such as random forests, LIME, and SHAP are applied, as they struggle with complex feature interactions and managing noise in large datasets. We address this challenge by developing an integrated framework that combines Oblique Random Forests (ORFs) with a novel, permutation-based feature importance testing algorithm. ORFs extend traditional random forests by employing oblique decision boundaries through linear combinations of features, thereby capturing intricate interactions inherent in neuroimaging data. Our feature importance testing method, NEOFIT, rigorously quantifies the significance of each feature by generating null distributions and corrected p-values. We first validate our approach using simulated datasets, establishing its robustness and scalability under controlled conditions. We then apply our method to classify sex from both voxel-wise structural MRI and cortical thickness data in humans and macaques, facilitating direct cross-species comparisons. ORFs achieves AUC > 0.80 on human data, and >0.70 on macaque data, while NEOFIT identifies statistically significant features aligned with sex-dimorphic neuroanatomy. Our results demonstrate that the proposed framework not only enhances classification performance but also provides clear, interpretable insights into the neuroanatomical features that distinguish sexes. These methodological advancements pave the way for improved diagnostic tools and contribute to a deeper understanding of the evolutionary basis of sex differences in brain structure.
18 Apr 2026
Read appraisal →Medicine
Effectiveness of cognitive behavioral therapy for depression in patients with multiple sclerosis: A systematic review and meta-analysis
BACKGROUND: The efficacy and methodologies of cognitive behavioral therapy (CBT) for treating depression in multiple sclerosis (MS) require further comprehensive research. METHODS: Two researchers conducted independent literature searches across 5 databases, utilizing the keywords "multiple sclerosis," "cognitive behavioral therapy," and "depression," without any restrictions on publication year. The Cochrane Risk of Bias Tool 2.0 was utilized to assess the included studies. Statistical analyses were conducted using standardized mean difference as the effect size with Stata 17.0 software. RESULTS: This meta-analysis included 22 studies. The results indicate that, regardless of whether delivered face-to-face, online, or via telephone, both individual and group CBT effectively alleviate depression in patients with MS. Significant improvements in depressive symptoms were observed starting from the second month of intervention and persisted for up to 6 months post-intervention. CONCLUSION: Patients with MS can flexibly choose the delivery modality of CBT based on their individual circumstances while engaging in treatment for around 2 months. They are encouraged to reengage in CBT within 6 months post-intervention to ensure the continuity and stability of depression treatment outcomes. Healthcare providers can offer various CBT delivery modalities based on the individual preferences and circumstances of patients with MS, while developing corresponding plans to enhance the effectiveness of depression management.
18 Apr 2026
Read appraisal →Neuron
Spatial and single-cell transcriptomic atlas of human suprachiasmatic nucleus
The suprachiasmatic nucleus (SCN) is considered the master pacemaker of the circadian clock in mammals, but our current knowledge of the SCN is mostly based on rodent studies. Here, we report a comprehensive molecular and cellular atlas for the adult human SCN by spatial transcriptomics, single-nucleus RNA sequencing, and deep-learning-based histological analysis. We identified seven human SCN neuron subtypes with specific transcriptomes and spatial distributions. Comparison of humans, mice, and non-human primates revealed the conserved functional segregation within the SCN regulated by LIM homeobox 1 (LHX1) and RAR-related orphan receptor B (RORB). Furthermore, our results suggested that the human SCN has undergone marked reorganization of its neuropeptide signaling network. Finally, integrative analysis of human SCN transcriptomes and genome-wide association studies (GWASs) identified arginine vasopressin (AVP)/neuromedin S (NMS) subtype as the potential neuronal correlate for morningness chronotype. Thus, our spatial and single-cell transcriptomic atlas of the human SCN provided a basis for the understanding of neural and molecular mechanisms of the human circadian clock.
18 Apr 2026
Read appraisal →Journal of global health
The impact of disasters on maternal mental health: a systematic review
BACKGROUND: The vulnerability of maternal mental health is increasing with the frequent increase in natural and man-made disasters, yet evidence remains fragmented. We aimed to synthesise quantitative evidence on the impact of disaster exposure on maternal mental health and related pregnancy outcomes. METHODS: We systematically searched 12 electronic databases, nine sub-databases, and additional web sources for the studies published between 1995 and 2025. We included studies that assessed the impact of disaster exposure during pregnancy and provided quantitative outcomes for depression, anxiety, stress, posttraumatic stress disorder (PTSD), or adverse pregnancy outcomes. RESULTS: Of 18 604 studies, 24 studies met the inclusion criteria. Natural disasters were the highest group (n = 16), including earthquakes (n = 8), floods/cyclone/hurricane (n = 7), and wildfire (n = 1), followed by man-made disaster (n = 7), including conflict/war/terrorism (n = 5), nuclear disaster (n = 1), plane crash (n = 1), and war (n = 1), and one hybrid disaster. Disaster showed a consistent increase in the risk of depression, anxiety, stress, and PTSD among pregnant women, with effects persisting up to six years post-disaster event. Across studies, disaster exposure increased maternal mental health problems by approximately 30% to over 200%, and PTSD odds increased seven to 10-fold among highly exposed pregnant women. Three studies reported that both depression and PTSD during pregnancy were associated with adverse pregnancy or early child outcomes, suggesting potential intergenerational effects. CONCLUSIONS: Disasters exert enduring and profound impacts on maternal mental health, with consequences cascading across both mother and child. Urgent, cost-effective and disaster-informed policies are needed to mitigate such long-term impacts. REGISTRATION: PROSPERO: CRD42023445559.
17 Apr 2026
Read appraisal →Psychonomic bulletin & review
Speaker effects in language comprehension: An integrative model of language and speaker processing
The identity of a speaker influences language comprehension through modulating perception and expectation. This review explores speaker effects and proposes an integrative model of language and speaker processing that integrates distinct mechanistic perspectives. We argue that speaker effects arise from the interplay between bottom-up perception-based processes, driven by acoustic-episodic memory, and top-down expectation-based processes, driven by a speaker model. We show that language and speaker processing are functionally integrated through multi-level probabilistic processing: prior beliefs about a speaker modulate language processing at the phonetic, lexical, and semantic levels, while the unfolding speech and message continuously update the speaker model, refining broad demographic priors into precise individualized representations. Within this framework, we distinguish between speaker-idiosyncrasy effects arising from familiarity with an individual and speaker-demographics effects arising from social group expectations. We discuss how speaker effects serve as indices for assessing language development and social cognition, and we encourage future research to extend these findings to the emerging domain of artificial intelligence (AI) speakers, as AI agents represent a new class of social interlocutors that are transforming the way we engage in communication.
17 Apr 2026
Read appraisal →The Cochrane database of systematic reviews
Amyloid-beta-targeting monoclonal antibodies for people with mild cognitive impairment or mild dementia due to Alzheimer's disease
RATIONALE: Alzheimer's disease is a neurodegenerative disorder and the most common cause of dementia. Aggregated amyloid-beta protein deposits are implicated in its pathogenesis. Amyloid-beta-targeting monoclonal antibodies (sometimes represented as Aβ-mAbs) are potentially disease-modifying for Alzheimer's disease: through the clearance of amyloid in the brain, they may slow cognitive and functional decline. OBJECTIVES: To assess the clinical benefits and harms of amyloid-beta-targeting monoclonal antibodies aducanumab, bapineuzumab, crenezumab, donanemab, gantenerumab, lecanemab, ponezumab, remternetug, and solanezumab in people with mild cognitive impairment or mild dementia due to Alzheimer's disease. SEARCH METHODS: We searched CENTRAL, MEDLINE (PubMed), Embase, and two clinical trials registries (Clinicaltrials.gov and WHO International Clinical Trials Registry Platform), and we undertook reference checking and citation research. The most recent search date was 7 August 2025. ELIGIBILITY CRITERIA: We included randomised controlled trials (RCTs) that lasted at least 12 months and compared amyloid-beta-targeting monoclonal antibodies with placebo or no treatment in people with mild cognitive impairment or mild dementia due to Alzheimer's disease. We included both parallel-group and cluster designs. OUTCOMES: Our outcomes of critical importance were: cognitive function; dementia severity; functional ability; any amyloid-related imaging abnormality (ARIA), which includes oedema (E) and haemorrhage (H); any symptomatic ARIA E and H; symptomatic brain haemorrhage; serious adverse events; and any-cause mortality. We analysed data at 12, 18, 24, and over 24 months of treatment. RISK OF BIAS: We used the Cochrane risk of bias tool RoB 2 to assess the risk of bias in outcomes of critical importance. SYNTHESIS METHODS: We meta-analysed results for each outcome within each comparison using the inverse variance method and the random-effects model. We used GRADE to assess the certainty of evidence for each outcome as very low, low, moderate, or high. INCLUDED STUDIES: Overall, we included 17 studies with 20,342 participants. The mean age of participants in the studies ranged from 70 to 74 years. Seven studies enroled only participants with mild dementia, and one study enroled only participants with mild cognitive impairment. The remaining studies included a mixed population. The mean duration of participants' cognitive impairment ranged from 17 to 52 months. The 17 studies assessed seven different amyloid-beta-targeting monoclonal antibodies: aducanumab (n = 3), bapineuzumab (n = 4), crenezumab (n = 2), donanemab (n = 1), gantenerumab (n = 4), lecanemab (n = 1), and solanezumab (n = 2). All used placebo as a comparison. Eleven studies lasted 18 months, four lasted 24 months, and two lasted more than 24 months. All studies were funded by the pharmaceutical industry. SYNTHESIS OF RESULTS: Below, we report the results of the studies at 18 months. Cognitive function Compared to placebo, amyloid-beta-targeting monoclonal antibodies probably result in little to no difference in cognitive function as measured by the ADAS-Cog (Alzheimer's Disease Assessment Scale-Cognitive) scale (standardised mean difference (SMD) -0.11, 95% confidence interval (CI) -0.16 to -0.06; 13 studies, 9895 participants; moderate certainty). Dementia severity Amyloid-beta-targeting monoclonal antibodies may result in little to no difference in dementia severity as measured by the CDR-SB (Clincal Dementia Rating Sum of Boxes) scale (SMD -0.12, 95% CI -0.24 to -0.00; 9 studies, 8053 participants; low certainty). Functional ability Amyloid-beta-targeting monoclonal antibodies probably result in little to no difference in functional ability as measured on the ADCS-ADL (Alzheimer's Disease Cooperative Study - Activities of Daily Living) scale (SMD 0.09, 95% CI 0.03 to 0.16; 3 studies, 3478 participants; moderate certainty) and may result in a small increase in functional ability if measured with the ADCS-iADL (Alzheimer's Disease Cooperative Study-Instrumental Activities of Daily Living) scale (SMD 0.21, 95% CI 0.10 to 0.32; 1 study, 1252 participants; low certainty) or ADCS-ADL-MCI (Alzheimer's Disease Cooperative Study - Activities of Daily Living for Mild Cognitive Impairment) scale (SMD 0.23, 95% CI 0.12 to 0.33; 4 studies, 2802 participants; low certainty). Adverse events Amyloid-beta-targeting monoclonal antibodies probably result in a small increase in the occurrence of any ARIA E (ARD (absolute risk difference) 107 more per 1000, 95% CI 77 more to 148 more; 11 studies, 13,595 participants; moderate certainty) and probably little to no difference in symptomatic ARIA E (ARD 29 more per 1000, 95% CI 22 more to 38 more; 2 studies, 3522 participants; moderate certainty) or symptomatic ARIA H (ARD 4 more per 1000, 95% CI 1 fewer to 31 more; 1 study, 1795 participants; moderate certainty). Three studies assessing any ARIA H showed heterogeneous results (I2 = 81%), which prevented pooled analysis. At 18 months, amyloid-beta-targeting monoclonal antibodies do not increase serious adverse events (ARD 6 more events per 1000, 95% CI 10 fewer to 26 more; 9 studies, 11,904 participants; high certainty) or overall mortality (ARD 2 more events per 1000, 95% CI 3 fewer to 11 more; 7 studies, 9733 participants; high certainty). We judged the overall risk of bias as low for the outcomes of serious adverse events and mortality. We had some concerns about the overall risk of bias for efficacy outcomes, mainly due to the risk of functional unblinding (i.e. participants and investigators correctly guessing whether a participant is receiving the active drug or placebo because of noticeable side effects). AUTHORS' CONCLUSIONS: The effect of amyloid-beta-targeting monoclonal antibodies on cognitive function and dementia severity at 18 months in people with mild cognitive impairment or mild dementia due to Alzheimer's disease is trivial, while on functional ability, it is small at best. Amyloid-beta-targeting monoclonal antibodies increase the risk of amyloid-related imaging abnormalities. Both desirable outcomes and adverse events were inconsistently reported in the studies included in the review. Successful removal of amyloid from the brain does not seem to be associated with clinically meaningful effects in people with mild cognitive impairment or mild dementia due to Alzheimer's disease. Future research on disease-modifying treatments for Alzheimer's disease should focus on other mechanisms of action. FUNDING: This Cochrane review was funded in part by the Drug and Medical Devices Governance Area, Regione Emilia-Romagna, Bologna, Italy. The publication of this article was supported by "Ricerca Corrente" funding from the Italian Ministry of Health. REGISTRATION: Protocol (2025): PROSPERO registration number CRD420251114325.
17 Apr 2026
Read appraisal →PloS one
Exploration of schizophrenia-associated gene modules using graph theory, co-expression networks, and dimensionality reduction.
The PsychENCODE consortium has generated a comprehensive multi-omic dataset from human brain samples, spanning healthy individuals and those with neuropsychiatric disorders. In this study, we focus exclusively on schizophrenia and analyze PsychENCODE transcriptomic data to construct gene regulatory and co-expression networks, aiming to uncover biologically and clinically relevant gene modules. We apply three independent analytical approaches to the same dataset. First, we preprocess the data using Surrogate Variable Analysis (SVA) to correct for latent variation and normalize gene expression. For graph-based analysis, we use Pearson correlation and the igraph package to construct gene co-expression networks and apply Prim's algorithm to generate minimum spanning trees (MST) and compute centrality measures. Next, we implement Weighted Gene Co-expression Network Analysis (WGCNA), assuming a scale-free topology, to identify modules associated with schizophrenia traits. Dimensionality reduction is performed using Principal Component Analysis (PCA), with visualization aided by t-distributed Stochastic Neighbor Embedding (t-SNE) and Multidimensional Scaling (MDS). Functional enrichment is carried out using Gene Ontology (GO) and KEGG pathway databases. Each method reveals distinct yet complementary biological signatures associated with schizophrenia. The igraph-based approach highlights differentially expressed genes (DEGs) with high centrality, uncovering hub genes involved in mRNA export, synaptic signaling, and ion transport. WGCNA identifies co-expression modules strongly correlated with schizophrenia diagnosis, enriched for immune response, histone modification, and mRNA surveillance pathways. PCA isolates key genes contributing to diagnostic variance, with enrichment in neurotransmitter release cycles and cytokine signaling. Collectively, these results underscore the involvement of immune, synaptic, and epigenetic processes in schizophrenia and demonstrate the power of using multiple, orthogonal analytical lenses.
17 Apr 2026
Read appraisal →JMIR human factors
Comparing Images of Depression in Mass Media and AI-Generated Pictures: Mixed Methods Study
BACKGROUND: Images play an important role in reducing stigma related to mental health, which often is distorted in the media. In recent years, generative artificial intelligence (AI) has been used to generate images related to mental health. However, first reports suggest that AI-generated images do not depict mental health conditions accurately. In-depth studies on the topic of mental health representations in AI-generated images are still missing. OBJECTIVE: The main objective of this study is to analyze and compare the visual representation of depression in mass media and in AI-generated images. METHODS: The methodologies used were discussion groups (15 participants) and a quasi-experimental online survey (792 interviewees), aimed at people with depression and young people. RESULTS: The results showed that both the images used in the media and those generated by AI reproduced stereotypes and stigmas about depression. However, participants considered AI-generated pictures to be more stereotypical, stigmatizing, and more likely to have a negative impact on people with depression. In contrast, media images were considered more appropriate, realistic, inclusive, and that better reflected the relationship between gender and depression. Statistically significant differences were observed between the control and test groups in both people with depression and young people (P<.001), indicating that when people were aware of what images were AI-generated, they tended to reject them to a greater extent. CONCLUSIONS: Considering the current trend toward the widespread use of AI in mental health communication, it is crucial to promote closer collaborations between science journalists, AI developers, and mental health experts, including patients' associations, as well as a shift toward user-participatory AI design.
17 Apr 2026
Read appraisal →NeuroImage
Convergent neural signatures of optimal creative performance: A systematic review and meta-analysis
The neural architecture that supports optimal creative performance across diverse contexts remains unclear. To address this question, we conducted a quantitative meta-analysis to identify convergent patterns of brain activation associated with enhanced creativity and to examine their relationships with behavioral improvements. A total of 33 neuroimaging studies involving 965 participants were included, encompassing four enhancement contexts: creativity training, domain-specific expertise, trait-level creativity, and positive stimulus conditioning. Across these contexts, superior creative performance (Hedges' g = 0.668) was consistently characterized by increased activation in the left middle frontal gyrus (LMFG), left superior frontal gyrus (LSFG), and left inferior parietal gyrus (LIPG), together with decreased activation in the right precuneus (95 % CI [0.508, 0.828]). Meta-regression analyses revealed that longer intervention duration and greater professional experience predicted stronger LSFG and LIPG activation, whereas age and sex ratio showed no significant effects. LSFG activation was attenuated during convergent thinking tasks, suggesting task-specific modulation of creative neural circuits. These findings provide systematic evidence for a convergent neural signature of superior creative performance across multiple enhancement pathways, offering empirical targets for future training and neuromodulation-based interventions to optimize creative potential.
17 Apr 2026
Read appraisal →JMIR mental health
Determinants of Digital Health Literacy Among Patients With Serious Mental Illness: Cross-Sectional Survey
BACKGROUND: Individuals with serious mental illness increasingly use digital devices and the internet to access health information and services but often face challenges when navigating digital tools, which may limit the benefits they receive from online health resources and digital health care services. OBJECTIVE: The objective of our study was to assess digital health literacy among individuals with serious mental illness and identify factors influencing this literacy. METHODS: Participants were recruited, using convenience sampling, from 2 psychiatric clinics, 1 day-care center, and 4 halfway houses in Taipei, Taiwan, between May 2024 and February 2025. Self-reported data were collected using a survey that incorporated the eHealth Literacy Scale, the Attitudes Toward Computer/Internet Questionnaire, and the Mobile Device Proficiency Questionnaire. Generalized linear modeling was applied to identify factors associated with digital health literacy. RESULTS: Among 255 participants included in the analysis, 83.5% (n=213) reported owning at least 1 digital device. Digital health literacy was significantly lower among individuals who reported greater perceived difficulty in using digital tools (B=-1.533, 95% CI -2.350 to -0.717; P<.001) and higher distrust in online information (B=-0.986, 95% CI -1.916 to -0.056; P=.04). By contrast, greater mobile device proficiency (B=0.144, 95% CI 0.008-0.281; P=.04) and self-efficacy (B=1.777, 95% CI 0.376-3.177; P=.01) were positively associated with digital health literacy. CONCLUSIONS: Despite widespread device ownership, digital health literacy was varied and generally suboptimal among patients with serious mental illness. Perceived difficulty and distrust emerged as major barriers; proficiency and self-efficacy facilitated higher literacy. These findings highlight the need for mental health professionals to integrate tailored digital skills training, confidence-building strategies, and ongoing support into digital health interventions for individuals with serious mental illnesses.
16 Apr 2026
Read appraisal →JMIR formative research
Development of Virtual Mental Health Stepped Care Service for a Heart Failure Remote Management Program: Qualitative Descriptive Study
BACKGROUND: Depression is highly prevalent yet undertreated among people living with heart failure, indicating barriers to mental health services. Although various digital mental health interventions have been developed to detect, treat, and manage depression in this population, these interventions have seen limited integration into clinical care and a lack of implementation research. Stepped care is a service innovation that may promote the implementation of these technologies into clinical settings, but few studies have examined how these services are designed in clinical settings. OBJECTIVE: This study aimed to identify strategies to address health system barriers to accessing mental health care from the perspective of people living with heart failure, clinicians, and researchers, and to incorporate these strategies into the design of a virtual mental health stepped care service within a heart failure remote management program. METHODS: A qualitative description study was conducted using purposive recruitment of people living with heart failure, clinicians, and researchers from a heart failure remote patient management program. As part of a service design approach, semistructured interviews explored potential strategies to address barriers to accessing mental health services. Two researchers coded the data descriptively and constructed themes to guide the development of a virtual stepped care service. RESULTS: A total of 22 participants were interviewed, comprising 13 people living with heart failure and 9 clinicians and researchers. Six themes were identified, comprising 4 requirements and 2 foundational principles. The requirements were to (1) adopt a collective approach to identify distress across methods, people, and time points; (2) maintain a referral-based approach; (3) rely on existing mental health human resources; and (4) offer patient choice among various mental health care options. These requirements were supported by two principles: (1) building on organizational strengths and (2) reducing treatment burden. Based on these findings, a virtual stepped care service was developed, incorporating a depression screening module, referral-based workflows, and, where clinically appropriate, patient choice in treatment selection. CONCLUSIONS: The stakeholder-informed design of this virtual stepped care service contributes to the limited literature on stepped care service design and demonstrates how such models can be tailored to their intended contexts. Although each component was designed to address health system barriers to mental health care for people living with heart failure, resource limitations may constrain the balance between feasibility and quality of care. Future research should evaluate the acceptability of this model among people living with heart failure and clinicians.
16 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 →Biological psychiatry
From Snapshots to Stable Outcomes: Resting-State Functional Magnetic Resonance Imaging-Based Prognosis of Functioning in Patients With Psychosis Risk or Recent-Onset Depression
BACKGROUND: Early recovery of functioning is critical for favorable outcomes in psychotic and affective disorders. Transdiagnostic brain activity patterns may capture pathways for poor outcomes before clinical manifestation, thereby supporting timely prevention and intervention. METHODS: Using machine learning, we evaluated the transdiagnostic prognostic value of resting-state functional magnetic resonance imaging fractional amplitude of low-frequency fluctuations (fALFF) (slow-5 and slow-4 sub-bands) for functional outcomes in patients at clinical high risk for psychosis (n = 217) or with recent-onset depression (n = 198) from the multisite PRONIA (Prognostic Tools for Early Psychosis Management) study. Leave-site-out cross-validation assessed the geographic generalizability of models across disability and symptom domains, with outcomes defined as snapshots at 9- or 18-month follow-up or across both time points. We examined diagnosis-specific performance, generalization to recent-onset psychosis (n = 140), and negative symptoms and the added value of fALFF over clinical prognostication. RESULTS: Transdiagnostic models predicting stable good functioning across follow-ups showed up to 10% higher balanced accuracy (BAC) than snapshot models. Decreased slow-5 fALFFs in the default mode network, executive control network (ECN), and dorsal attentional network (DAN) and increased fALFF in the salience network, ECN, and DAN predicted impairment with BAC = 67% (sensitivity = 65%, specificity = 70%, p < .001). This model generalized to recent-onset psychosis (BAC = 62%, sensitivity = 64%, specificity = 59%, p < .001) and predicted (BAC = 65%, sensitivity = 66%, specificity = 65%, p < .001) and was mediated by negative symptoms. Slow-5-based models improved prognostic accuracy over expert ratings in disability (BACraters = 66%, BACraters+slow-5 = 75%, W = 1680, p < .001) and symptom (BACraters = 61%, BACraters+slow-5 = 71%, W = 1444, p < .001) domains. CONCLUSIONS: We highlighted the prognostic value of fALFF for functional impairment in psychosis risk and early depression. Leveraging trajectorial information, we identified candidate imaging biomarkers to improve prognostication, thereby supporting personalized prevention and recovery strategies.
16 Apr 2026
Read appraisal →Journal of affective disorders
Comorbid clinical and biomarker outcomes in patients with obsessive-compulsive disorder: An umbrella review and evidence map
BACKGROUND: Obsessive-compulsive disorder (OCD) is a chronic psychiatric condition with diverse comorbidities, yet outcome research remains limited. Thus, we aimed to systematically review existing meta-analyses of OCD-associated comorbid outcomes and assess the strength of evidence. METHODS: We conducted an umbrella review of meta-analyses, searching PubMed/MEDLINE, Embase, CINAHL, and Google Scholar up to July 7, 2025. Although we intended to include all studies related to OCD-associated comorbid outcomes, only observational studies were available. Effect sizes were recalculated using random-effects models, and all estimates were converted to equivalent standardized mean differences (eSMD) with 95 % confidence intervals (CIs). The methodological quality of the included reviews was assessed using the AMSTAR 2 tool, and the class of evidence (CE) for each association was graded according to predefined criteria (Class I-IV). RESULTS: We identified 17 meta-analyses that included 373 primary studies, covering 52 outcomes in 156,161 participants across 40 countries. Patients with OCD were associated with autistic traits outcomes, including restricted and repetitive behaviors (eSMD, 1.66 [95 % CI, 1.25-2.07]; CE = IV), and total autism-spectrum quotient score (1.40 [1.06-1.81]; CE = IV). OCD also showed associations of varying strength with several other outcomes, including suicidal attempt (0.64 [0.47-0.81]; CE = II) and toxoplasmosis infection (0.37 [0.15-0.59]; CE = III). Furthermore, quality of life was decreased across multiple domains in individuals with OCD (emotional, -1.47 [-1.96 to -0.98]; CE = II; global, -0.79 [-1.02 to -0.55]; CE = II; work/social, -1.21 [-1.72 to -0.69]; CE = III). CONCLUSION: Our study highlights the comprehensive impact of OCD, indicating associations with autism, suicidality, social-cognitive deficits, impaired quality of life, and neurobiological markers. STUDY REGISTRATION: PROSPERO CRD420251124380.
16 Apr 2026
Read appraisal →Journal of affective disorders
The effects of Omega-3 supplementation on stress, anxiety, depression, sleep quality, and everyday memory in individuals with psychological distress: A randomized, double-blind, placebo-controlled trial
BACKGROUND/OBJECTIVES: Psychological stress, anxiety, depression, memory impairment, and poor sleep quality are prevalent issues that affect individuals' quality of life and overall health. Omega-3 polyunsaturated fatty acids (PUFAs) have been suggested to have mood-regulating effects. However, there is lack of data on the effect of omega-3 supplementation on depression, stress, anxiety, everyday memory, and sleep quality on Saudi population. Therefore, this study aimed to evaluate the impact of omega-3 supplementation on stress, anxiety, depression, everyday memory, and sleep quality in individuals with severe psychological distress. METHODS: A randomized, double-blind, placebo-controlled trial was conducted involving 64 participants with high levels of stress, anxiety, depression, memory issues and with poor quality of sleep with 32 allocated to the intervention group and 32 to the control group. The participants received either omega-3 supplementation (500 mg EPA + 250 mg DHA) or a placebo daily for three months. Pre- and post-intervention assessments were conducted using validated scales, including the Perceived Stress Scale (PSS), Generalized Anxiety Disorder-7 (GAD-7), the Patient Health Questionnaire-9 (PHQ-9), the Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale (ESS), and the Everyday Memory Questionnaire (EMQ). RESULTS: Significant improvements were observed in the intervention group regarding stress, anxiety, depression, sleep quality, and memory outcomes (p < 0.001 for PSS, GAD-7, PHQ-9, PSQI, and EMQ). Between-group comparisons showed statistically significant reductions in post-intervention scores for the intervention group versus the control group. Regression analysis revealed strong predictive relationships between pre- and post-scores, particularly for stress and depression. CONCLUSIONS: Omega-3 supplementation resulted in notable psychological and cognitive improvements, suggesting its potential as a safe and effective adjunct for managing stress, mood disorders, and sleep disturbances among participants with psychological distress. Further research should explore how individual responses are influenced by baseline inflammation and omega-3 status. CLINICALTRIALS: gov: NCT07157241.
16 Apr 2026
Read appraisal →Journal of affective disorders
Exploring temperamental and clinical predictors of lithium treatment outcomes in bipolar disorder using diverse machine learning approaches
BACKGROUND: Lithium is a core treatment for bipolar disorder (BD), yet clinical response varies across patients. Testing accessible predictors of lithium response is critical for personalization strategies in real-world settings. METHODS: In this cross-sectional study, 179 individuals with BD were assessed using a broad range of standardized clinical instruments and the Brief TEMPS-M to evaluate affective temperaments. Lithium response was retrospectively classified via the Alda Scale. Multivariate linear regressions and three supervised machine learning classifiers (random forest, elastic net, and XGBoost) were applied to identify predictors of treatment response. Class imbalance was addressed by down-sampling during training. RESULTS: Responders (12.3 % of participants) were more likely to be employed, diagnosed with BD-II, had fewer lifetime depressive and hypomanic episodes, fewer suicide attempts, and significantly lower anxious temperament scores. In multivariable models, lifetime psychotic and mixed features, as well as higher counts of hypomanic and manic episodes, were independently associated with poorer lithium response. Machine learning models confirmed the importance of these variables, possibly with remarkably high predictive validity (random forest AUC = 1.00; elastic net and XGBoost F1 = 0.86). Hyperthymic temperament was positively associated with lithium response, whereas cyclothymic and depressive traits were associated with poorer lithium response. CONCLUSIONS: The present exploratory study should be intended as a primer for larger samples and more balanced replication studies employing reliable machine learning techniques, allowing for firm conclusions about suggestive clinical predictors of lithium response in BD, and their complex interaction.
16 Apr 2026
Read appraisal →Journal of affective disorders
Development and validation of machine learning models to predict PTSD at multiple time points in hospitalized trauma patients
BACKGROUND: Posttraumatic stress disorder (PTSD) is a severe trauma-related mental disorder with high global burden. Early identification remains challenging, particularly in developing regions. Leveraging routinely collected clinical biomarkers may offer a scalable solution for timely risk detection. METHOD: This multicenter study utilized two independent prospective trauma cohorts to develop and externally validate machine learning models for predicting PTSD at 1, 3, and 6 months post-trauma. The development cohort consecutively enrolled 382 hospitalized trauma patients (Nov 2023-Dec 2024), while the external validation cohort included 204 patients (Oct 2021-Nov 2022). Predictors comprised psychosocial variables and routine clinical biochemical markers, including inflammatory indicators, liver and renal function markers, and hematological parameters. Five models were constructed (Logistic Regression, Random Forest, SVM, XGBoost, and Neural Network), with performance evaluated by AUC and feature importance interpreted via SHAP. RESULTS: Support Vector Machine achieved the best performance at 1 month (AUC = 0.84), while Random Forest models performed best at 3 and 6 months (AUC = 0.78 and 0.87). SHAP analysis indicated that ASDS were the most important predictors at all time points, with feature importance shifting over time. Acute stress and psychological state were dominant at 1 month, fearful experiences with physiological responses at 3 months, and chronic psychological burden and emotional state at 6 months. CONCLUSIONS: PTSD can be reliably predicted using psychosocial data and routine clinical biochemical markers. These findings support the integration of clinically accessible biological data into early PTSD risk screening, particularly in resource-limited settings.
15 Apr 2026
Read appraisal →Journal of affective disorders
Adverse psychosocial work exposures and 2-year incident prescription of psychotropic medications: a prospective French study using the national healthcare reimbursement database
OBJECTIVE: Despite the abundant literature on the associations between psychosocial work exposures and mental health outcomes, prospective studies using medico-administrative data are lacking. The objective was to study the associations between various psychosocial work exposures and incident prescription of psychotropic medications. METHODS: The study relied on three waves (2010, 2012, 2014) of the French ESPS survey (Health, Health Care and Insurance Survey) (baseline data), linked to the national database of healthcare reimbursements (follow-up data). Each survey wave was conducted among a randomly selected national sample of people aged 18+. The study included a total of 8689 observations among 7695 workers without psychotropic medication prescription within the 6 months preceding survey wave. Ten psychosocial work exposures (quantitative demands, tensions with the public, freedom, possibilities for learning new things, colleague support, recognition, salary satisfaction, job insecurity, temporary contract, and redundancy plan) were assessed at each survey wave. Covariates included gender, age, marital status, employee/self-employed worker, private/public sector, occupation, and full/part-time work. The outcome was psychotropic medication prescription within the 2 years following survey wave. Mixed effects Cox regression modelling was used. RESULTS: Seven of the ten psychosocial work exposures were associated with incident psychotropic medication prescription. Associations were observed more with incident prescription of antidepressants and anxiolytics than with hypnotics/sedatives. The number of psychosocial work exposures increased the risk linearly, except for hypnotics/sedatives. No gender interaction was found for any of these associations. CONCLUSION: The findings may support that poor psychosocial working conditions increase the risk of subsequent psychotropic medication prescription.
15 Apr 2026
Read appraisal →Biomedical physics & engineering express
EEG-CMT: spatial-temporal representation of EEG for emotion recognition using convolutional neural networks and vision transformers
Background. Recent researches on electroencephalogram (EEG) based emotion recognition face challenges in effectively mapping the spatial positional relationships of EEG acquisition electrodes. Additionally, conventional models struggled to simultaneously capture both fine-grained temporal-spatial features and long-range dependencies in EEG signals.New method. To address these limitations, we propose a novel EEG data processing method that incorporates spatial relative position encoding and a hybrid neural architecture integrating convolutional neural networks (CNNs) with self-attention mechanisms. This approach systematically encodes the spatial topology of electrodes to enhance the representation of temporal-spatial information. CNNs are employed to extract localized temporal-spatial micro-patterns, while self-attention modules model global contextual dependencies across extended sequences, thereby enhancing model's representational capacity.Results. The experimental results and feature visualizations demonstrate that our method achieves state-of-the-art (SOTA) subject-dependent performance on two benchmark emotion recognition datasets, reaching an average accuracy of 97.51% on the SEED dataset and 96.13% on the SEED-IV dataset. Moreover, the learned spatial features align well with known neuroscientific patterns of emotional processing. Comprehensive ablation studies further validate the necessity and effectiveness of both the spatial-encoded data processing strategy and the hybrid architecture design.Comparison with Existing Methods. Compared to other hybrid neural network models, our proposed method (EEG-CMT) achieves the highest classification accuracy. Specifically, it outperforms baseline algorithms by margins ranging from 0.86% to 11.43% on the SEED dataset, and from 9.49% to 39.52% on the SEED-IV dataset.Conclusions. The proposed method effectively addresses key limitations in existing EEG-based emotion recognition models by jointly leveraging spatial topology and hybrid modeling techniques. These innovations significantly improve the model's ability to recognize emotions from EEG data and provide neural interpretable insights, offering a promising direction for future research in affective brain-computer interfaces.
15 Apr 2026
Read appraisal →Food & function
The effect of anthocyanins through diet and supplementation on cognitive function in older adults: a multi-centre randomised controlled clinical trial
Anthocyanins, a flavonoid subclass present in certain blue, purple and red fruits and vegetables, have potential neuroprotective properties due to their anti-inflammatory, antioxidant, and signalling effects. Clinical and epidemiological studies of anthocyanins provide promising evidence for attenuation of memory loss and improved cognitive function. We investigated the effects of anthocyanins, provided through diet or supplementation, on cognitive function and related parameters. A multi-centre, 24-week randomised, parallel 3-arm clinical trial was conducted in participants aged 60-85 years with self-reported memory complaints and scoring ≤13 on the Memory Index Score within the Telephone Montreal Cognitive Assessment (T-MoCA). Participants were randomised to high anthocyanin diet, freeze-dried product derived from blackcurrants (250 mg anthocyanins per day), or control (placebo). The primary outcome was auditory episodic memory functioning. Secondary outcomes were additional cognitive functions, subjective memory complaints and self-reported depression symptoms, blood pressure, inflammatory biomarkers, lipid profile, vascular and microvascular endothelial function tests. Mixed linear modelling evaluated changes over time using both intention-to-treat and completer analyses. Of 110 participants enrolled (mean age 69.2 years SD 6.7; 36 males, 74 females), 94 completed the study. There was no treatment × time effect on any primary or secondary outcomes. We postulate that the potential anti-inflammatory, antioxidant, and signalling effects of anthocyanins were insufficient to significantly modulate any outcomes due to participants' overall good health and lack of relevant cognitive decline. We recommend future interventional studies focus on subjects with diagnosed cognitive impairment, such as MCI and dementia, and those with more severe comorbidities, particularly chronic inflammatory conditions. Trial Sponsor: National Health and Medical Research Centre (NHMRC) Dementia Collaborative Research Centre.
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 →JAMA
Restrictive vs Liberal Physical Restraint Strategies in Critically Ill Patients: The R2D2-ICU Randomized Clinical Trial
IMPORTANCE: The effect of wrist-strap physical restraints on outcomes in patients receiving mechanical ventilation in the intensive care unit (ICU) remains uncertain. OBJECTIVE: To investigate the effect of a low-use wrist-strap physical restraint strategy in critically ill patients receiving invasive mechanical ventilation. DESIGN, SETTING, AND PARTICIPANTS: Open-label randomized clinical trial conducted across 10 ICUs in France. Between January 5, 2021, and January 2, 2024, 405 adult patients who had initiated invasive mechanical ventilation within the previous 6 hours and were expected to require ventilation for at least 48 hours were enrolled. Follow-up was completed on May 17, 2024. Statistical analysis was conducted from June 1, 2025, to December 15, 2025. INTERVENTIONS: Patients were randomized to undergo either a restrictive, low-use physical restraint strategy (wrist straps avoided unless necessary because of severe agitation, defined as a Richmond Agitation-Sedation Scale score of ≥3 [on a scale from -5 (unresponsive) to 4 (combative)]; n = 201) or a liberal, high-use strategy (wrist straps applied systematically and reassessed daily; n = 204). Discontinuation of restraints was allowed in patients who were awake or extubated without delirium (measured via the Confusion Assessment Method for the ICU). MAIN OUTCOMES AND MEASURES: The primary outcome was the number of days alive without coma or delirium during the first 14 days after randomization. Secondary outcomes included incidence of self-extubation and day-90 mortality. RESULTS: Among 396 patients with available primary outcome data, the median (IQR) age was 65 (56-73) years, 245 (62%) were male, and the median (IQR) Sequential Organ Failure Assessment score was 7 (4-10). The mean days alive without coma or delirium were 6.67 days (95% CI, 5.69-7.65) in the low-use strategy group and 6.30 days (95% CI, 5.35-7.24) in the high-use strategy group (adjusted mean difference, 0.37 days [95% CI, -0.71 to 1.46]; P = .51). Self-extubation occurred in 18 patients (9.2%) in the low-use strategy group and 17 (8.5%) in the high-use strategy group, and day-90 mortality was 37.2% and 41.0%, respectively. CONCLUSIONS AND RELEVANCE: In this randomized clinical trial, among adult patients receiving mechanical ventilation in the ICU, a low-use wrist-strap physical restraint strategy compared with a high-use strategy did not reduce days free of delirium or coma at 14 days. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04273360.
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 →Epidemiology and psychiatric sciences
Cross-cultural validity of the generalized anxiety disorder 7-item scale (GAD-7): a systematic review and standardized assessment
AIMS: The Generalized Anxiety Disorder 7-Item Scale (GAD-7) is a brief self-reported measure for screening for anxiety symptoms. However, the evidence about its cross-cultural validity is fragmentary and usually focused on specific settings. Therefore, we aimed to critically review and synthesize the existing evidence about the cross-cultural validity of the GAD-7. METHODS: We conducted a systematic review of studies assessing the cross-cultural validity of the GAD-7 in following the PRISMA guidelines. Additionally, the quality of the studies was assessed following the COSMIN guidelines, and the quality of the evidence was assessed with the GRADE. Data were synthesized narratively. RESULTS: Out of 1,965 unique records, 9 unique studies were deemed eligible for the COSMIN appraisal and the narrative synthesis (total sample: 11,894, 53.7% females and 20 different cultural groups). Most studies (7) had adequate quality and showed evidenced of the unitary structure of the GAD-7 across cultural groups. In 4 studies also assessing possible cultural bias, the effect on the general score was deemed negligible. CONCLUSIONS: The evidence about the cross-cultural validity of the GAD-7 is very limited. Although more research is needed, the evidence available shows that the GAD-7 could be a cross-culturally valid tool for the assessment of anxiety symptoms in clinical contexts and epidemiological studies. Until new high-quality evidence will be available, these results would constitute a key first step for supporting the use of the GAD-7 in multi-cultural clinical settings and to inform clinical, public health and global health decision making in relation to anxiety.
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 clinical pharmacology
Clozapine prescribing in treatment-resistant schizophrenia - an updated systematic literature review of barriers and facilitators among clinicians
BACKGROUND: Increased rates of clozapine prescribing are essential to improving timely patient access within treatment-resistant schizophrenia (TRS) management. The extent of geographical variation in its use suggests it is possible to develop interventions to increase clinician engagement. To inform intervention development, a contemporary review of barriers and facilitators to increased clozapine prescribing is required. We aimed to conduct a systematic review of research addressing barriers and facilitators to clozapine prescribing among clinicians within TRS management. METHODS: The review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed, Embase, CINAHL, and PsycINFO were searched from inception to July 2025. Results were synthesized qualitatively. RESULTS: Fifteen studies were included. Barriers related to clinicians, patients and carers, and healthcare institutions. Primary clinician-related barriers included insufficient knowledge of, and confidence in, managing clozapine treatment and the associated administrative burden. Primary patient-related barriers included concerns regarding patients' willingness to consistently adhere to clozapine treatment and associated monitoring requirements. A lack of dedicated systems of care to facilitate clozapine initiation and shared community care were the leading institutional barriers. Major facilitators included improved education for clinicians, access to point-of-care testing, and increased availability of dedicated clozapine clinics. CONCLUSION: Most barriers to systematically increasing clozapine prescribing rates are beyond the influence of individual prescribers. Instead, structural interventions focusing on (i) reducing the administrative burden associated with establishing clozapine treatment, (ii) increasing access to standardised training and supervision opportunities, and (iii) providing longitudinal support to clinicians when managing clozapine treatment, are required.
13 Apr 2026
Read appraisal →Journal of medical Internet research
Views of People With Psychosis About Algorithm-Based Relapse Prediction and Data Sharing: Qualitative Study
BACKGROUND: Preventing relapses of psychosis is difficult and important. Digital remote monitoring (DRM) systems are being developed and tested to support this. Increasingly, these systems use algorithm-based relapse prediction. Hence, understanding stakeholder views about algorithmic prediction is crucial. Existing qualitative work has explored health professionals' views, but very few studies have examined the perspectives of people with psychosis on this topic. OBJECTIVE: This paper aimed to provide an in-depth examination of the views of people with psychosis regarding algorithmic relapse prediction within a DRM system that incorporates active symptom monitoring and passive sensing data. METHODS: People with psychosis (n=58) were recruited from 6 geographically distinct areas of the United Kingdom. They participated in semistructured qualitative interviews exploring their views about using a DRM system that predicts psychosis relapse based on a machine learning algorithm. Transcripts were analyzed using reflexive thematic analysis. People with lived experience of psychosis were involved extensively in study design, analysis, and reporting. RESULTS: Findings were described across 4 themes. First, accuracy was a prominent theme. Participants emphasized that transparency about algorithm sensitivity and specificity is crucial and discussed the risks of the relapse prediction algorithm producing false positives (flagging that someone was relapsing when they were not) and false negatives (missing actual relapses). In both cases, participants said that errors may be partially mitigated through a human-in-the-loop approach (theme 2), with DRM blended with human oversight, from clinicians or a dedicated digital monitoring team, and calibrated based on service user, carer, and clinician feedback. The third theme, trust, fears, and choice, noted the interplay between users' trust in the DRM system and their relationship with the clinical team. This theme described participants' fears about potential overreactions (hospitalization or excessive medication) or underreactions (no additional support) from the clinical team in response to algorithm-generated relapse predictions. It emphasized the importance of retaining choice around the use of relapse detection algorithms and the sharing of personal data. The final theme described participants' views about the benefits of using a relapse prediction algorithm, including facilitating early intervention, triaging care according to need, minimizing human bias in assessment, and efficiency in saving staff time. CONCLUSIONS: People with psychosis acknowledged potential benefits of algorithm-assisted relapse prediction for receiving timely or efficient care, but with several caveats. Algorithm-generated relapse alerts need to be sufficiently accurate and must be interpreted, with understanding of their limitations, by a trustworthy human who is aware of the relevant context. Algorithm-based relapse predictions should only be used with valid consent, in a way that promotes and respects the autonomy and voice of service users and avoids increasing the use of excessive restriction.
11 Apr 2026
Read appraisal →JMIR aging
Facilitators and Barriers to Digital Self-Management in Older Adults With Depression: COM-B and Theoretical Domain Framework Qualitative Study
BACKGROUND: Depression in older adults presents unique challenges in self-management. Digital tools, such as mobile health (mHealth) apps, have the potential to support this population. This study explored the facilitators and barriers to digital self-management in older adults with depression to inform the design of effective mHealth apps. OBJECTIVE: This study aims to explore the facilitators and barriers to digital self-management in older patients with depression with the aim of informing the design and development of mHealth apps for older adults. METHODS: A purposive sampling method was used to recruit 25 older patients with depression from July to September 2024. Semistructured interviews were conducted to capture real-life experiences. Directed content analysis ensured objective and accurate data interpretation, and the Capability, Opportunity, Motivation, and Behavior (COM-B) model and the Theoretical Domains Framework were applied to identify facilitators and barriers related to behavior. RESULTS: Six themes were identified based on the COM-B model and the Theoretical Domains Framework: perception of illness and accumulation of personal experience; dual challenges of cognitive abilities and physical limitations; integration of digital technologies and acceptance differences; social influences and access to and utilization of support resources; environmental constraints and accommodations; and intertwined influences of beliefs, emotions, and motivation. A total of 13 barriers and 11 facilitators were identified. CONCLUSIONS: Digital self-management in older adults with depression is complex and influenced by multiple interrelated factors. Effective mHealth apps must integrate the cognitive, emotional, and social contexts of patients to provide user-friendly, personalized solutions.
11 Apr 2026
Read appraisal →Journal of medical Internet research
Digital Discourse, Secondary Victimization, and Psychological Harm: Mixed-Methods Analysis of System Justification in the #MeToo Movement
BACKGROUND: The #MeToo movement, initiated in 2006 and amplified on social media in 2017, mobilized women worldwide to share experiences of sexual harassment and assault online. While the movement increased awareness, it also revealed deep social divisions in digital spaces. Supportive discussions promoted solidarity and healing, whereas antagonistic responses reinforced backlash and secondary victimization. In India, the Indian Entertainment Industry (IEI) became a focal point where survivors' disclosures highlighted structural gender inequalities. These polarized reactions function as digital-health signals, reflecting stigma, psychosocial distress, and conditions that shape women's safety and mental well-being. Examining these narratives as indicators of public health risk helps identify patterns of structural inequity and secondary mental health burdens among survivors. OBJECTIVE: This study examined online discourse surrounding #MeToo to identify forms of system-justifying narratives on social media and to assess how #MeTooIndia exposed institutional inequities within the IEI. METHODS: This mixed-methods study comprised 2 components. In study 1, natural language processing was applied to analyze global #MeToo Twitter (subsequently rebranded X) discourse. From an initial corpus of 350,000 tweets, 205,082 were preprocessed, and sentiment and stance detection analysis identified 18,416 tweets expressing negative attitudes toward the movement. Latent Dirichlet allocation topic modeling extracted 22 topics, 12 of which aligned with system-justification categories, revealing distinct lexical and semantic patterns related to gender, institutional, and power dynamics. Two trained coders manually annotated a subsample to ensure conceptual clarity and interrater reliability. Study 2 involved qualitative, semistructured interviews with 20 academic experts in film, gender, and media studies to gather opinions on how #MeTooIndia influenced institutional discourse in the IEI and how these dynamics translate into digital and mental health risks. RESULTS: Analysis of #MeToo Twitter discourse in study 1 identified 4 primary forms of system justification: by gender, by the institutional system, by backlash, and by victim-blaming. Gender and institutional system justifications were the most prevalent. Study 2 reinforced these findings, revealing how experts perceived #MeTooIndia as both empowering and constrained by entrenched institutional and cultural barriers. Together, our findings highlight the dual function of social media in promoting collective advocacy while reproducing conditions linked to gender-based violence, psychological stress, and reduced help-seeking-key digital and mental health concerns. CONCLUSIONS: This mixed-methods study reveals that digital discourse surrounding #MeToo often sustains existing gender and institutional hierarchies rather than dismantling them. Across Twitter data and expert interviews, gender and institutional system justifications emerged as dominant narratives, highlighting how online spaces can reinforce structural inequities while appearing progressive. Although #MeToo amplified visibility and awareness, its potential for lasting institutional change remains limited. These findings underscore the need for trauma-informed digital governance, public health recognition of online hostility as a psychosocial risk, and frameworks that situate digital activism to institutional reforms that support safety and mental well-being.
11 Apr 2026
Read appraisal →Psychological medicine
Interventions to prevent antipsychotic-induced weight gain and metabolic complications in individuals with a first-episode psychosis and minimal antipsychotic exposure: a systematic review and meta-analysis
BACKGROUND: Rapid weight gain commonly occurs following the onset of first-episode psychosis (FEP), leading to cardiometabolic disease. Most weight gain in FEP occurs in the first 3 months of treatment, offering a critical window for prevention. Despite this, most studies aiming to prevent antipsychotic-induced weight gain include people with chronic illness or people who have had lengthy exposure to antipsychotic medication. We aimed to synthesize and analyze the literature on interventions aimed at preventing antipsychotic-induced weight gain. METHODS: We conducted a systematic review in PsycInfo, MEDLINE, CINAHL, and EMBASE of studies that examined the effectiveness of interventions in preventing antipsychotic-induced weight gain in FEP. We examined their effect on weight gain and a range of cardiometabolic markers. RESULTS: We screened 2,092 articles, 13 of which were eligible. Behavioral interventions, all three of which consisted of a multidisciplinary team approach, resulted in a mean of 3.05 kg less weight gain than treatment-as-usual (95% CI 1.36 kg to 4.73 kg). Pharmacological interventions displayed marked clinical and statistical heterogeneity, with each of the seven trials in adults using a different pharmacological intervention. Few studies collected comprehensive data on metabolic health. Only two pharmacological studies, and five studies in total, have been published since 2010. CONCLUSIONS: Despite the importance of preventing weight gain in FEP, there have been few recent studies investigating this topic. Our results indicate that multidisciplinary team interventions are effective in preventing weight gain in FEP and should be offered to all patients.
11 Apr 2026
Read appraisal →JMIR human factors
Evaluation of Wellby, a Cocreated Mobile App and Wearable to Support Stress Management and Overall Well-Being: Mixed Methods Acceptability and Usability Study
BACKGROUND: Digital well-being support tools can offer adolescents tailored interventions embedded in their digital environments. However, there is a lack of high-quality, evidence-based digital interventions specifically designed for young people's well-being needs. Wellby is a mobile app and wearable device cocreated with Irish secondary school students to support stress management and overall well-being. OBJECTIVE: This study aimed to evaluate the acceptability and usability of the Wellby system using the technology acceptance model, focusing on ease of use, perceived usefulness, and behavioral intention among Irish secondary school students. METHODS: A mixed methods acceptability and usability study was conducted with Irish secondary school students (n=43) across 3 schools: 2 mainstream secondary schools and 1 Youthreach center for early school leavers. Students accessed the Wellby mobile app for 8 weeks and received the custom wearable device for the final 4 weeks (April-May 2024). Wellby included mood tracking, to-do lists, educational resources, text-based health coaching, and heart rate variability biofeedback with guided breathing exercises. Acceptability and usability were evaluated using the user version of the Mobile App Rating Scale (uMARS), structured focus groups, and app engagement analytics. Secondary measures included baseline well-being questionnaires (Perceived Stress Scale, Pittsburgh Sleep Quality Index, and EPOCH Measure of Adolescent Well-Being). RESULTS: Among survey respondents (n=29), uMARS subscale scores were high: information quality (mean 4.2/5.0), functionality (4.1/5.0), and aesthetics (4.1/5.0). The perceived impact subscale showed the highest scores for well-being awareness (3.8/5.0) and help-seeking intention (3.9/5.0). Of survey respondents who participated in prior co-design sessions, 88% (21/24) and 83% (20/24) felt the wearable and app, respectively, aligned with their co-design feedback. App engagement more than doubled following the introduction of the wearable and revealed that the wearable tab was most accessed (643/1688, 38% of interactions), followed by the home (430/1688, 25%), resources (338/1688, 20%), and coaching (277/1688, 16%) tabs. Focus group feedback highlighted the mood tracker as the most valued feature and identified technical improvements, including better battery life, enhanced Bluetooth connectivity, and more personalization options. CONCLUSIONS: This study demonstrates that cocreated digital well-being tools can achieve high acceptability and usability with Irish secondary school students. Applying the technology acceptance model effectively captured student experience through mixed methods feedback. Students particularly valued self-tracking and personalization features over coaching or educational features. These findings highlight the importance of aligning digital technologies with adolescents' needs and preferences, such as features that encourage increased autonomy and identity formation. Future iterations of Wellby should address the technical limitations while continuing the involvement of students to adapt to their developmental needs and maintain high acceptability.
11 Apr 2026
Read appraisal →Journal of epidemiology and community health
Utilisation of in-person and telehealth care among US adults with loneliness and social isolation
BACKGROUND: Telehealth has become a critical tool for improving access to care among populations facing physical barriers to in-person care. However, its potential role in delivering care to adults with social barriers remains underexplored. We examined whether adults with loneliness or social isolation are more likely to use telehealth care versus in-person care compared to adults without loneliness or social isolation. METHODS: This cross-sectional study analysed data for US adults in the 2021 Medical Expenditure Panel Survey. Loneliness and social isolation were measured using the UCLA Loneliness Scale and the Berkman-Syme Social Network Index. Three indicators of healthcare utilisation were used as outcomes: in-person care only, telehealth care only and a combination of both. RESULTS: Among adults experiencing loneliness and social isolation, the likelihood of using only in-person care was lower by 4.1 (95% CI -6.1 to -2.1) and 2.8 (95% CI -5 to -0.6) percentage points. In contrast, the likelihood of using only telehealth care was higher by 2.6 (1.1-4.2) and 5.4 (3.9-6.9). The likelihood of using both in-person and telehealth care was also higher by 2.3 (0.7-3.9) and 5.2 (3.7-6.7), but telehealth was used more frequently than in-person care. Furthermore, telehealth utilisation for mental health services was higher among adults experiencing loneliness or social isolation. CONCLUSION: Compared with adults who are not lonely or socially isolated, adults experiencing loneliness and social isolation are more likely to use telehealth and less likely to use in-person care. Telehealth may serve as a viable alternative for individuals facing social challenges in accessing in-person services.
11 Apr 2026
Read appraisal →Psychological medicine
Inflammatory cytokine alterations in genetic and clinical high risk groups of psychosis: a systematic review and network meta-analysis
BACKGROUND: Inflammation has been implicated in psychosis, but its role in individuals at clinical (CHR) and genetic (GHR) high-risk remains unclear. We therefore conducted a network meta-analysis (NMA) to compare circulating cytokine levels across CHR, GHR, and healthy control (HC) groups. METHODS: We systematically searched multiple databases up to February 2025, extracting cytokine levels (plasma/serum) from CHR, GHR, and HC groups. Standardized mean differences (SMDs) with 95% confidence intervals (CIs) were estimated using random-effects models. Given that no direct head-to-head comparisons between CHR and GHR were available, indirect comparisons were performed through the common comparator (HC). The transitivity assumption was assessed by comparing key study and participant characteristics across comparisons. RESULTS: Thirty studies were included (CHR: 1601, GHR: 675, HC: 1980). NMA estimates indicated higher IL-6 levels in CHR compared with GHR, while IL-6 and IL-1β levels were lower in GHR compared with HC. In pairwise subgroup analyses, CHR converters showed higher IL-13 levels than non-converters. The evidence network was sparse and star-shaped, with all CHR-GHR estimates relying exclusively on indirect comparisons. CONCLUSIONS: This study represents the first NMA to synthesize cytokine alterations in individuals at high risk for psychosis using indirect evidence. Elevated IL-6 in CHR individuals suggests immune activation, whereas reduced IL-6 in GHR may reflect a distinct immune profile. Increased IL-13 levels in converters highlight potential involvement of Th2-related pathways during transition to psychosis. However, the sparse nature of the evidence network necessitates cautious interpretation of the findings, and larger, standardized multi-center studies are required for confirmation.
11 Apr 2026
Read appraisal →JMIR mental health
Predicting Momentary Suicidal Ideation From Smartphone Screenshots Using Vision-Language Models: Prospective Machine Learning Study
BACKGROUND: Passive smartphone sensing shows promise for suicide prevention, but behavioral metadata (GPS, screen time, and accelerometry) often lacks the contextual information needed to detect acute psychological distress. Analyzing what people actually see, read, and type on their phones-rather than just usage patterns-may provide more proximal signals of risk. OBJECTIVE: This study aimed to test whether vision-language models (VLMs) applied to passively captured smartphone screenshots can predict momentary suicidal ideation (SI). METHODS: Seventy-nine adults with past month suicidal thoughts or behaviors completed ecological momentary assessments (EMA) over 28 days while screenshots were captured every 5 seconds during active phone use. We fine-tuned open-source VLMs (Qwen2.5-VL [Alibaba Cloud], LFM2-VL [Liquid AI]), and text-only models (Qwen3 [Alibaba Cloud]) to predict SI from screenshots captured in the 2 hours preceding each EMA. We evaluated performance with temporal and subject holdouts. RESULTS: The analytic sample comprised 2.5 million screenshots from 70 participants. Temporal holdout models achieved strong discrimination at the EMA level (AUC=0.83; AUPRC=0.77), with image-based models outperforming text-only models (AUC=0.83 vs 0.79; 95% CI 0.003-0.07). Subject holdout generalization was near chance (AUC≈0.50), though a simple lexical screening method retained modest discrimination (AUC=0.62). Smaller models performed comparably to larger models, supporting feasible on-device deployment. CONCLUSIONS: Screen content predicts short-term SI with clinically meaningful accuracy when models are personalized but does not generalize across individuals. These findings support a 2-stage clinical architecture, coarse lexical screening for new patients, with personalized VLM-based monitoring after a calibration period. On-device inference may enable privacy-preserving deployment.
10 Apr 2026
Read appraisal →JMIR research protocols
Cultural Adaptation of a Digital Mobile App for Bipolar Disorder (PolarUs): Protocol for a Qualitative Co-Design Study
BACKGROUND: Bipolar disorder (BD) affects approximately 40 million people worldwide and is a chronic, potentially disabling mood disorder. Although effective treatments exist, access to evidence-informed psychosocial care remains limited, particularly for culturally and linguistically diverse populations, contributing to persistent global treatment gaps. Digital mental health interventions (DMHIs), such as smartphone apps, offer a promising means to improve access to self-management support and quality of life (QoL), an outcome prioritized by people with BD and in clinical guidelines. However, most apps for BD lack quality and are not culturally adapted or co-designed with people with BD, limiting relevance and engagement. PolarUs (mobile app) is an evidence-informed DMHI developed using co-design with people with BD. The app is structured on the core 14 domains from the Quality of Life in BD scale, the only BD-tailored scale, combined with psychoeducation on self-management strategies and QoL. A recent pilot study demonstrated promising QoL, clinical, and feasibility outcomes. OBJECTIVE: This study aims to culturally and linguistically adapt the PolarUs app into French, Chinese, and Spanish for the North American context using qualitative and co-design methods. METHODS: Guided by community-based participatory research principles, whereby end users are engaged throughout the research process, and the Ecological Validity Framework of Bernal et al, we will engage advisory groups of people with lived experience from each linguistic community throughout the cultural adaptation process. Semimonthly virtual meetings will support systematic cultural adaptation of the self-management strategies, affirmations, and resources while maintaining fidelity to core evidence-based components. This will include cultural tailoring of app content and the identification of culturally appropriate resources. Advisory groups will also contribute to the cointerpretation of findings and the co-design of culturally appropriate recruitment and implementation strategies of PolarUs for a future clinical trial. Meetings will be recorded and coanalyzed as research data with advisory groups using qualitative reflexive thematic analysis to capture advisory group perspectives and experiences. RESULTS: This study was funded in October 2024. As of January 31, 2026, we enrolled 7 participants, and the results are expected to be published in the fall of 2026. CONCLUSIONS: The findings will support the development of a culturally appropriate DMHI for BD for additional linguistic communities, advance cultural adaptation methodologies, and inform preparation for a future clinical trial. This study will produce the first culturally adapted, BD-specific DMHI developed through co-design using a community-based participatory research approach with multilingual end users from traditionally underserved communities, advancing equitable access, engagement, and scalability of DMHIs for BD and digital health care more broadly.
10 Apr 2026
Read appraisal →Scientific reports
SPG601-associated modulation of resting-state EEG and improvement in executive function in a fragile X syndrome randomized controlled crossover study
Fragile X syndrome (FXS) is the most common inherited cause of intellectual disability and autism spectrum disorder. Despite extensive research, no targeted treatments exist for the core symptoms of FXS. SPG601 represents the first BK channel activator to enter clinical testing for FXS, designed to restore synaptic function by correcting specific ion channel dysfunction downstream of fragile X messenger ribonucleoprotein protein (FMRP) loss. We conducted a randomized, double-blind, placebo-controlled, 2-period balanced crossover study in 10 adult men with genetically confirmed full-mutation fragile X syndrome. Participants received single doses of SPG601 800 mg and placebo separated by a 1-week washout period. Given the first in FXS nature of the study, the safety and tolerability of SPG601 were evaluated as a primary outcome. Additional endpoints included resting-state EEG power spectral density analysis across predefined frequency bands and auditory-evoked gamma oscillations combined with cognitive assessments using validated instruments and clinical global impressions scales. Among EEG measurements, excessive high frequency gamma band activity has been most extensively validated as a biomarker across species in FXS and has demonstrated correlation with severity of the human FXS phenotype and therefore served as the primary neurophysiologic endpoint. SPG601 was well tolerated with a favorable safety profile. SPG601 demonstrated significant modulation of resting-state EEG power spectral density compared to placebo. Significant treatment effects were observed for gamma power (F = 5.20, p = 0.023), alpha2 power (F = 17.43, p < 0.001), and theta power (F = 7.06, p = 0.008). SPG601 also significantly modulated aperiodic EEG slope (F = 5.28, p = 0.022), indicating alterations in the broadband 1/f component reflecting excitation-inhibition balance. Cognitive improvement was observed in the NIH Toolbox Flanker Inhibitory Control and Attention Test across multiple scoring metrics (p = 0.027). No significant effects were observed on auditory-evoked gamma measures. This study provides the first clinical evidence that SPG601 produces significant neurophysiological changes in FXS, accompanied by a cognitive enhancement in executive function.
10 Apr 2026
Read appraisal →JMIR mHealth and uHealth
Promoting Self-Regulated Social Media Use on Smartphones With a Mobile Intervention App (Wellspent): Randomized Controlled Trial
BACKGROUND: Problematic social media use has been linked to reduced well-being and impulse control difficulties. While digital self-control apps show potential for reducing general app usage, they often lack customization, leading to limited effectiveness and increased user resistance. Their impact on problematic social media use remains uncertain. OBJECTIVE: This study evaluates the effectiveness of the Wellspent app, a customizable mobile intervention app designed to promote self-regulated social media use by targeting user-defined problematic app use and offering tailored behavioral nudges. METHODS: In a 3-week randomized controlled trial, 70 iPhone users (mean age 26.2, SD 5.6 years; 47/70, 67% female), regularly using at least 1 social media app, were randomly assigned to an intervention (n=35) or control group (n=35). The intervention group received personalized full-screen reminders with the option to quit or continue social media app use whenever an app session exceeded a self-defined time limit. Participants completed weekly online surveys measuring problematic social media use, problematic smartphone use, self-efficacy, and daily screen time on their most problematic app. Linear mixed models tested intervention effects. RESULTS: While no significant reduction in problematic social media use or increase in self-efficacy was observed, the intervention group showed a significant reduction in daily screen time on their most problematic app by approximately 29 minutes (estimate=-29.35, SE 6.84, 95% CI -42.79 to -15.99; P<.001), and a significant decrease in perceived problematic smartphone use (estimate=-0.46, SE 0.18, 95% CI -0.80 to -0.11; P=.01). CONCLUSIONS: The Wellspent app demonstrated short-term efficacy in reducing problematic smartphone use. By allowing users to tailor interventions to their personal goals, the app shows promise as a self-directed tool to support healthier digital habits. Further research should explore long-term effects and feature-specific impacts.
9 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 →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Digital and AI-enhanced psychosocial interventions in breast cancer patients: a systematic review and meta-analysis
PURPOSE: Digital and AI-supported psychosocial interventions are increasingly implemented to address psychological distress among women with breast cancer. However, their effectiveness and sustainability remain uncertain. This review aimed to systematically evaluate randomised controlled trials (RCTs) assessing the impact of digital and AI-enhanced psychosocial interventions on stress, anxiety, depression, and health-related quality of life (HRQoL). METHODS: Following PROSPERO registration and PRISMA 2020 guidelines, comprehensive searches of multiple databases were conducted up to June 2025. Eligible studies included RCTs involving breast cancer populations and reporting validated psychosocial outcomes. Risk of bias was assessed using the Cochrane RoB 2.0 tool, and certainty of evidence was evaluated with GRADE. Random-effects meta-analyses using standardised mean differences (Hedges' g) were conducted to pool outcomes measured with different instruments. RESULTS: Thirty-five RCTs (n ≈ 7551 participants) met inclusion criteria. Pooled analyses indicated modest but consistent reductions in stress, anxiety, and depressive symptoms, alongside improvements in HRQoL. Interventions grounded in cognitive behavioural therapy, mindfulness, or psychoeducation produced the most reliable outcomes. AI-assisted and blended formats tended to show stronger effects compared with static or minimally interactive platforms. However, heterogeneity in intervention design, short follow-up periods, reliance on self-reported outcomes, and small single-centre samples limited certainty. Cultural adaptation and representation from low- and middle-income countries were minimal. CONCLUSION: Digital and AI-supported psychosocial interventions show promise in reducing distress and improving HRQoL among women with breast cancer. Evidence certainty remains moderate, constrained by methodological limitations. Larger, multicentre trials with longer follow-up and diverse populations are needed to establish sustainability, generalisability, and clinical integration. RELEVANCE: These findings suggest that digital and AI-enhanced psychosocial interventions may serve as adjuncts to conventional care, with potential to improve access to supportive oncology services. Further high-quality, long-term studies are needed before informing guidelines, policy, or widespread implementation. TRIAL REGISTRATION: PROSPERO (CRD420251074813; registered 16 June 2025).
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 →JMIR mHealth and uHealth
Effectiveness of a Telecare-Based Intervention Program in Supporting Informal Caregivers of Community-Dwelling Older Adults in Reducing Stress Levels: Randomized Controlled Trial
BACKGROUND: Informal caregivers are essential in supporting community-dwelling older adults, especially as global populations age. However, caregiving responsibilities often result in high stress, depressive symptoms, and diminished quality of life. Traditional support services are fragmented, and many programs overlook caregivers' emotional and informational needs. Telecare-the remote delivery of health and social care via digital technologies-offers a promising approach to provide accessible, personalized, and timely support. OBJECTIVE: This study evaluated the effectiveness of a telecare-based intervention program, developed by a health-social partnership team, in improving psychological well-being and caregiving outcomes among informal caregivers of community-dwelling older adults. We hypothesized that caregivers in the intervention group would show greater improvements in stress, self-efficacy, depression, quality of life, and caregiving burden compared to those receiving usual care. METHODS: A single-blind randomized controlled trial was conducted from January to December 2023, with 75 informal caregivers providing care to older adults (aged ≥60 y) for at least 4 hours per week. Participants were randomized to an intervention group (n=38, 51%) or a control group (n=37, 49%). The 3-month intervention included (1) biweekly online nurse case management supported by a multidisciplinary health-social team, (2) personalized WhatsApp videos on caregiving skills, and (3) a password-protected caregiver website offering resources and peer discussion. The control group attended 6 in-person educational sessions at a community center. Outcomes such as stress, self-efficacy, depression, quality of life, and caregiving burden were measured at baseline and postintervention. RESULTS: Participants had a mean age of 65.5 (SD 9.617) years, and 81.3% (61/74) were female. Baseline characteristics were similar between groups. Compared to controls, the intervention group showed significantly higher self-efficacy (t=1.98; P=.04) and lower depression (t=-2.24; P=.03) at follow-up. Within-group improvements in the intervention group were also observed for stress, depression, self-efficacy, mental quality of life, and caregiving burden. No significant between-group differences were found for stress, quality of life, or caregiving burden. No significant within-group improvements were observed in the control group. CONCLUSIONS: This study provides novel evidence that a digitally delivered, nurse-led telecare program-integrating health and social services-can improve psychological outcomes among informal caregivers. These findings have practical implications for health systems seeking scalable, low-barrier solutions to support caregivers. Telecare models may be a valuable complement to traditional services, enhancing caregiver resilience and promoting aging in place while alleviating long-term strain on institutional care systems.
9 Apr 2026
Read appraisal →Archives of endocrinology and metabolism
Cushing's syndrome: a systematic review of psychiatric and cognitive symptoms in case studies
OBJECTIVE: Cushing's syndrome, characterized by chronic hypercortisolism, is associated with various health risks, including psychiatric symptoms and cognitive impairments. This systematic review of case studies aimed to map and categorize these symptoms. MATERIALS AND METHODS: We hypothesized that a broader range of psychiatric and cognitive manifestations would be observed beyond anxiety, depression, and memory impairment. The review followed PRISMA guidelines and was preregistered in PROSPERO (CRD42024433186). We conducted searches in PsycINFO, Embase, PubMed, and Scopus, identifying 273 potentially relevant studies. After screening, 66 studies were included, comprising 74 cases (81% female; mean age 35.7 years, range 13-81). RESULTS: revealed that 93% of cases presented psychiatric complaints, including depression (39.2%), psychosis/schizophrenic symptoms (35.1%), suicidal ideation/attempts (20.3%), anxiety (17.5%), panic attacks (2.7%), and post-traumatic stress disorder (1.4%). Cognitive complaints were reported in 32% of cases, primarily as general cognitive complaints (18.9%), memory impairment (9.5%), and attentional deficits (5.4%). An overlap of psychiatric and cognitive symptoms was observed in 26% of cases. CONCLUSION: This review underscores the clinical relevance of symptoms such as mania, psychosis, and suicidal behavior in Cushing's syndrome, which are often underreported. Individualized clinical assessment informed by these case studies is crucial for comprehensive management that extends beyond the typical focus on depression and memory. Moreover, greater awareness of the full spectrum of neuropsychiatric manifestations in hypercortisolism is needed.
8 Apr 2026
Read appraisal →JMIR human factors
Use of Digital Peer Support for Employee Well-Being: Retrospective Analysis Across Five Large Employers
BACKGROUND: Anonymous, 24/7 digital peer support (DPS) offers a scalable solution to support employees' emotional well-being. Understanding sociobehavioral factors, such as timing of engagement and the impact of shared resources, can help employers and employee assistance programs (EAPs) integrate digital tools to better support workforce well-being. OBJECTIVE: This study examines (1) outcomes via overall sentiment changes among DPS users; (2) sociobehavioral differences between employees who accessed real-time, anonymous DPS during versus outside business hours; (3) differences in sentiment outcomes based on time of use; and (4) the impact of in-session resource sharing on sentiment improvement. METHODS: Using OpenAI's large language model (LLM) GPT-4o-mini with a few-shot learning approach, 24,818 anonymous chat conversations from 13,879 employees at 5 large employers were evaluated for subclinical sentiment variables, including loneliness, sadness, stress, anxiety, depression, despair, helplessness, and optimism. RESULTS: Distinct activity patterns were observed between employees during and outside business hours, with a median user age of 36 years. During business hours, employees reported higher baseline stress (Δ1.6%), whereas outside business hours, baseline depression (Δ1.7%) and loneliness (Δ1.2%) were higher. After DPS use, LLM-derived negative sentiment scores decreased (loneliness 46% reduced, sadness 45%, stress 46%, anxiety ~39%, depression ~40%, despair ~40%, and helplessness ~38%), and optimism increased (~77%). Outside business hours, users engaged more (~68% of all sessions), remained in sessions 19% longer, and discussed 13% more topics, whereas users during business hours reported greater improvements in depression (Δ2.3%), helplessness (Δ1.9%), and loneliness (Δ0.5%). Resource sharing was associated with greater improvements in loneliness (Δ2.9%), stress (Δ1.3%), anxiety (Δ1.9%), depression (Δ7.3%), despair (Δ3.0%), helplessness (Δ2.8%), and optimism (Δ8.8%), but not sadness. CONCLUSIONS: DPS complements employers' EAPs by addressing employee engagement gaps, reducing barriers to mental health care, and promoting emotional well-being among the workforce.
8 Apr 2026
Read appraisal →JMIR mHealth and uHealth
A Mobile App-Based Individualized Nonpharmacological Intervention for Behavioral and Psychological Symptoms in Dementia: Pilot Randomized Controlled Trial
BACKGROUND: Behavioral and psychological symptoms of dementia (BPSD) are common and negatively impact both individuals with dementia and their families. Although nonpharmacological interventions are recommended as the first-line treatments, their use in community settings is limited by access and caregiver resources. Existing approaches are often not individualized and rely on institutional or clinician-led delivery. OBJECTIVE: We developed a caregiver-initiated and individualized multimodal mobile app. The app delivers tailored nonpharmacological interventions-such as music therapy, exercise, and reminiscence therapy-based on each user's preferences and functional abilities. This study aimed to evaluate the effectiveness of this mobile app-based intervention in reducing BPSD in community-dwelling persons living with dementia. METHODS: This study used a single-blinded randomized controlled trial design. Participants were recruited from an outpatient clinic of a tertiary hospital, a dementia care center, and 5 home care service centers. A total of 36 dyads participated, each comprising a community-dwelling person living with dementia aged 60 years or older and their primary family caregiver. The dyads were randomly allocated to either the intervention or control group. The intervention group received a caregiver-initiated, multimodal, mobile app-based individualized intervention for 4 weeks, whereas the control group continued with usual care. The primary outcomes were overall BPSD, agitated behavior, and depression. The secondary outcomes were nighttime sleep efficiency and caregiver competency in managing BPSD. Assessments were conducted at baseline, immediately after the intervention, and at a 2-week follow-up. RESULTS: Of the 36 randomized dyads, 33 were included in the final analysis. Although the intervention group showed greater reductions in overall BPSD, agitated behavior, and depression after the intervention, no significant group-by-time interaction effects were observed in the total sample. In the subgroup analysis of participants with clinically significant baseline BPSD, a statistically significant improvement in overall BPSD was found in favor of the intervention group (β=-12.885, 95% CI -24.530 to -1.240; P=.03). No significant effects were observed for either nighttime sleep efficiency or competence in the management of BPSD. CONCLUSIONS: A mobile app-based individualized intervention may offer a flexible, caregiver-initiated approach to managing BPSD in home-care settings. While overall effects were limited, exploratory subgroup findings provided meaningful insights, indicating potential benefits for those with higher baseline symptom severity. The results highlight the need for further research on adaptive personalization and optimized intervention delivery to enhance the clinical effectiveness of digital dementia care.
8 Apr 2026
Read appraisal →Journal of medical Internet research
Electrocardiogram-Based Mental Stress Detection Amid Everyday Activities Using Machine Learning: Model Development and Validation Study
BACKGROUND: Frequent, sustained stress is linked to poor health and requires monitoring for early intervention. Electrocardiograms (ECG) are promising biomarkers because they can be recorded noninvasively and continuously using wearable devices. However, tracking stress with ECG is challenging because daily activities elicit responses similar to mental stress (MS), and various mental stimuli that individuals encounter complicate the use of machine learning (ML) models trained on a limited set of stressors. OBJECTIVE: We (1) evaluated the ability of ML models to distinguish MS episodes from a composite "no-stress" background, including rest and low- to moderate-intensity activities; (2) assessed their generalizability to new stressors and participants; and (3) tested robustness to lower sampling rates and fewer features, to explore their suitability for lightweight wearables. METHODS: We used a comprehensive ECG dataset sampled at 1000 hertz from 127 participants who underwent various mental stressors and engaged in diverse physical activities. A 30-second window was used to extract 55 features from time, frequency, nonlinear, and morphological domains. We trained a logistic regression (LR) model and an extreme gradient boosting (XGBoost) model, splitting the data into 60/20/20 for training, validation, and testing. Shapley additive explanation values were computed to explain model predictions. Additional analyses included leave-one-stressor-out; downsampling to 500, 250, and 125 hertz; a time-window sensitivity analysis; and reducing the number of features to as few as 5. RESULTS: XGBoost achieved an area under the receiver operating characteristic curve (AUROC) of 0.741 (95% CI 0.701-0.783) and an area under the precision-recall curve (AUPRC) of 0.706 (95% CI 0.658-0.753), compared with 0.724 (95% CI 0.678-0.772) and 0.691 (95% CI 0.639-0.742) for LR. The mean performance difference between XGBoost and LR was 0.017 for AUROC (95% CI 0.001-0.032) and 0.015 for AUPRC (95% CI -0.001 to 0.037; clustered bootstrap analysis using 2000 participant-level resamples), suggesting that LR performs comparably to the nonlinear XGBoost model. Both models were robust to downsampling and feature reduction (10 features retained >93% of performance). Extending the analysis window to 60 seconds improved model performance across all sampling rates, highlighting a trade-off between rapid detection and overall performance. When evaluating discrimination from physical activity, models achieved acceptable specificity for light physical activity (XGBoost: 0.787; LR: 0.794) but poor specificity for moderate physical activity (XGBoost: 0.418; LR: 0.444). Both models generalized to most unseen stressors, although performance varied across stressors, with limited transfer to the social-evaluative stressor. Feature importance analysis revealed fuzzy entropy and frequency-based features as key predictors. CONCLUSIONS: ML models can detect MS with high sensitivity and remain robust to lower sampling rates and fewer features. Generalization to novel stressors was stressor-dependent. Importantly, our results highlight challenges in distinguishing stress-related cardiac responses from those caused by physical exertion, revealing critical limitations of single-sensor ECG approaches for MS detection.
8 Apr 2026
Read appraisal →Global public health
Link between housing and community factors and mental health and psychological well-being of refugee parents and children in high-income countries: A systematic review
Whilst forced migration is known to significantly increase the risk of mental health problems, the influence of post-migration factors for refugee children and parents in high-income countries remains understudied. However, systemic post-migration factors have the potential to be modified by policy and practice to improve outcomes. This systematic review identifies the housing and community factors examined in research and their link to the mental health and psychological well-being of refugee parents and children in high-income countries. Adhering to the PRISMA guidelines, we searched the databases of PubMed, PsycINFO and Embase for studies published in peer-reviewed journals until the 17th of October 2025. Of the 11,588 studies identified, 30 were eligible for inclusion in this review. Our findings particularly suggest favourable mental health outcomes among those living with their families/relatives or foster care as opposed to reception centres, as well as those with social support. Perceived discrimination consistently showed an association with negative mental health outcomes. The findings suggest a gap in the evidence base focusing on parents, education and school-related factors. We recommend that policy makers provide family-based living arrangements and improve access to spaces for social support. Future research in this field will benefit from further sub-group analyses.
8 Apr 2026
Read appraisal →Psychological medicine
Joint effects of childhood adversity and genetic risk for psychosis on psychopathology in the UK Biobank
BACKGROUND: The individual effects of genetic factors and adverse childhood experiences (ACEs) on risk of psychosis, including schizophrenia (SCZ) and bipolar disorder (BIP), have been widely acknowledged, but their interaction effects on individual psychopathological symptoms remain unclear. METHODS: Based on data from 163,704 individuals in the UK Biobank, we investigated the joint effects of polygenic risk scores (PRSs) of SCZ and BIP and ACEs on psychopathology. ACEs status and 55 psychopathological symptoms from seven domains were measured retrospectively using an online mental health questionnaire in 2016. Recent genome-wide association studies for SCZ and BIP were combined with genotype data to generate PRSs. Logistic regression analyses were then conducted to explore univariate and joint main effects of PRSs and ACEs on psychopathological symptoms, as well as their additive and multiplicative interaction effects. RESULTS: The interaction mechanisms for PRSs and ACEs varied across symptom domains: additive interactions were observed on the depression (RERIBIP-ACEs = 0.20-0.25), anxiety (RERISCZ-ACEs = 0.20; RERIBIP-ACEs = 0.22-0.26), help-seeking (RERISCZ-ACEs = 0.24; RERIBIP-ACEs = 0.23), and cognition domains (RERISCZ-ACEs = -0.23 to -0.17), whereas multiplicative interactions were only detected on the psychotic (betaSCZ-ACEs = -0.543; betaBIP-ACEs = -0.181), mania (betaBIP-ACEs = -0.195), self-harm or suicide (betaSCZ-ACEs = -0.118), and cognitive domains (betaSCZ-ACEs = -0.204 to -0.157). CONCLUSIONS: The interplay mechanisms for genetic liability to SCZ and BIP and ACEs vary across symptom domains. This study reveals heterogeneity in gene-ACEs interaction mechanisms underlying psychosis and may provide personalized guidance for psychological care after ACEs.
8 Apr 2026
Read appraisal →Psychological medicine
Maternal communication and attachment following a group singing intervention for postnatal depression: findings from the SHAPER-PND trial
BACKGROUND: Postnatal depression (PND) can disrupt maternal communication during early interactions, affecting infant socioemotional development. Singing is a natural form of caregiver-infant communication and a promising intervention to enhance maternal well-being and bonding. However, its effects on observed communication and perceived attachment in clinical PND populations remain underexplored. METHODS: Within the Scaling-Up Health-Arts Programs: Postnatal Depression trial, 199 mothers with PND were randomized 2:1 to a 10-week group singing intervention (Breathe Melodies for Mums) or a non-singing community activity. One hundred participants (singing = 70; control = 30) completed video-recorded interactions at baseline, week 10, and week 36. Maternal speech was coded using the Parental Cognitive Attributions and Mentalization Scale (PCAMS) for mentalization, affective tone, and attentional focus. Perceived maternal attachment was assessed separately via self-report using the Maternal Postnatal Attachment Scale. RESULTS: At week 10, singing mothers showed greater improvement in communication with their infants than controls, with about 1.7-fold higher proportions of mentalizing comments (p = 0.01), 1.4-fold more infant-focused speech (p < 0.001), 2.4-fold less parent-focused speech (p < 0.001), and fivefold less negative speech (p < 0.001). These effects were maintained at week 36. Perceived attachment improved significantly across both groups (p < 0.001), but only singing mothers showed further gains from week 10 to week 36 (p = 0.02), indicating continued strengthening of attachment perceptions. CONCLUSIONS: Group singing enhanced maternal communication and perceived attachment in mothers with PND. Findings support community-based, arts-informed interventions as accessible approaches to strengthen early relational health and complement perinatal mental healthcare.
8 Apr 2026
Read appraisal →BMJ open
Adverse childhood experiences and cognitive outcomes among older adults in low-income and middle-income countries: a systematic review
OBJECTIVES: While adverse childhood experiences (ACEs) have been consistently linked to poorer cognitive outcomes in later life, far less is known about ACEs' contribution to dementia and cognitive ageing risk in low-income and middle-income countries (LMICs), despite their growing and disproportionate share of global Alzheimer's disease and related dementias (ADRD) burden. This study aimed to systematically review existing evidence on the association between individual ACEs and cognitive outcomes among adults aged 40 years and older in LMIC settings. DESIGN: Systematic review and narrative synthesis. DATA SOURCE: We searched Medline, Embase, PsycINFO and CINAHL from the inception of each database to January 2026. ELIGIBILITY CRITERIA: Observational studies examining the association between individual ACEs and cognitive outcomes among adults aged 40 and older in LMICs. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently screened each record, assessed risk of bias using the Joanna Briggs Institute critical appraisal tool and extracted data. Results were illustrated using descriptive forest plots and a narrative synthesis. RESULTS: Our systematic review included 14 studies, primarily from upper-middle-income countries, that assessed the relationship between individual ACEs and cognitive impairment (n=4) and function (n=10). 10 studies reported significant associations between ACEs and poorer cognitive function and increased risk of cognitive impairment. No study assessed the association between ACEs and ADRD. Parental death, neglect and mental health issues in the family showed consistent associations with cognitive outcomes, whereas experiencing hunger and poor health in childhood showed inconsistent associations. CONCLUSIONS: Our review corroborated evidence from high-income countries that exposure to ACEs is associated with a long-term risk of poorer cognitive outcomes among older populations in LMICs. However, the available literature remains limited, with most studies originating from upper-middle-income countries, few examining cognitive impairment and none assessing ADRD. These findings underscore substantial gaps in knowledge and highlight the need for research across diverse LMIC settings to clarify the role of broader psychosocial and contextual childhood experiences in shaping cognitive ageing and dementia risk. PROSPERO REGISTRATION NUMBER: CRD42024501816.
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 →Proceedings of the National Academy of Sciences of the United States of America
AI-derived therapeutic development of a serotonin receptor-targeting drug for the treatment of opioid use disorder
The opioid epidemic has led to a devastating loss of life nationwide. Of those dependent on opioids, many individuals desire to quit or reduce use, but their efforts are often unsuccessful given the powerful reinforcing properties associated with opioid drugs, including fentanyl. Here, we developed a therapeutic drug based on an AI-based platform, which was rationally designed to identify markers of dysregulation from human drug user postmortem brain tissue. Two top candidate compounds, GATC-021 and GATC-1021, were synthesized and validated with in vitro screening for target specificity. Thereafter, the drug candidates were examined for their effectiveness in modulating opioid reinforcement and intake. GATC-1021 emerged as the most efficacious compound as it significantly decreased fentanyl intake in both male and female rats, without any notable side effects. GATC-1021 was further found to modulate gene expression patterns in addiction-relevant brain regions following fentanyl-induced alterations. These findings validate GATC's AI-based platform with its polypharmacy-focused drug development approach and further support the clinical potential of GATC-1021 as a promising therapeutic for those suffering from opioid use disorder.
7 Apr 2026
Read appraisal →JMIR aging
AI-Enhanced Automatic Life Story Structuring for Reminiscence Therapy in Older Adults: Technical Feasibility Study
BACKGROUND: Storytelling interventions have demonstrated substantial potential in improving emotional well-being, cognitive function, and quality of life for older adults. However, its effectiveness is often limited by the challenges of processing disorganized and redundant life stories, which impose substantial cognitive demands on caregivers. Although storytelling interventions are a well-established therapeutic approach, current practices depend heavily on manual narrative organization, restricting both the scalability and consistency of treatment delivery. Prior research has primarily focused on validating the clinical outcomes of storytelling interventions, with insufficient attention given to technological solutions that could enhance narrative processing while preserving therapeutic integrity. Digital approaches to life story structuring remain underexplored, despite their potential to amplify storytelling benefits by reducing cognitive load and improving recall accuracy. OBJECTIVE: This study aims to design an event timeline generation algorithm to optimize the prior work of the Story Mosaic system. The optimized system enables (1) the automatic extraction of event elements from life narratives, (2) the automatic organization of fragmented life stories into structured timelines, and (3) the preservation of clinically relevant contextual details during compression. The goal is to reduce manual intervention costs while increasing treatment efficacy through artificial intelligence-driven narrative structuring. METHODS: We have designed a novel method, CARE event timeline (CARE-ET), which combines a temporal attention mechanism with graph-based event relationship modeling. Furthermore, we used the CARE-ET algorithm to optimize existing story collage systems. The system uses multifeature extraction technology to capture event clues from oral histories, prioritizes the 6 elements of events through a hierarchical attention mechanism, and uses adaptive compression algorithms to reduce redundancy while maintaining narrative continuity. To verify the effectiveness of the CARE-ET method, this paper adopts a multidimensional evaluation framework, which encompasses event summary assessment, timeline quality evaluation, and usability testing of the optimized system. RESULTS: The proposed CARE-ET algorithm outperforms the baseline in both narrative flow and temporal accuracy. The Story Mosaic system, optimized by the CARE-ET algorithm, underwent usability evaluation by 10 caregivers recruited for this study. Based on standardized assessment metrics, the system received an A rating for usability. The comprehensive experimental results demonstrate that the CARE-ET method can effectively structure fragmented narratives from older adults, enhancing the usability of the Story Mosaic system. CONCLUSIONS: The proposed method enables the structured extraction of representative event summaries, transforming disorganized life stories into an event timeline for caregiver-supported older adult well-being interventions. Future research should investigate longitudinal effects on cognitive preservation and explore integration with existing dementia care protocols. This work establishes a critical foundation for intelligent assistive technologies in geriatric mental health interventions.
7 Apr 2026
Read appraisal →PloS one
Identifying minimal risk factors for adolescent suicidal ideation and suicide attempts: A machine learning-optimized approach.
This study aimed to develop and validate a machine learning (ML) model to identify the minimal risk factors for adolescent suicidal behaviors, including suicidal ideation and suicide attempts. Data from the Korea Youth Risk Behavior Web-based Survey (2022-2023), including 90,813 adolescents aged 12-18 years, were analyzed. Using multidimensional risk factors spanning sociodemographic, physical and mental health, and behavioral domains, we applied a Random Forest model combined with recursive feature elimination to identify a minimal subset of risk factors (optimal features). Model performance for identifying suicidal ideation and predicting suicide attempts was evaluated via area under the curve (AUC), sensitivity, specificity, and accuracy metrics across the validation datasets. Sadness, loneliness, anxiety, and stress were identified as optimal features, achieving a high AUC, sensitivity, specificity, and accuracy in identifying suicidal ideation and predicting suicide attempts. Additional factors further improved the ML model's predictive performance for suicide attempts, achieving an AUC of 97.28%, sensitivity of 93.49%, specificity of 90.21%, accuracy of 91.85%, a PPV of 90.52%, and an NPV of 93.26%. This study demonstrated the efficacy of ML-driven approaches in identifying critical risk factors for adolescent suicidal behaviors. The findings highlight the potential of ML frameworks to transform suicide prevention strategies and improve mental health outcomes in adolescents.
7 Apr 2026
Read appraisal →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 →Neuroscience
Effects of single/dual-target transcranial alternating current stimulation on working memory in healthy individuals
This study compared the effects of single- and dual-target high-definition transcranial alternating current stimulation(tACS) on working memory and explored the associated neural mechanisms using functional near-infrared spectroscopy(fNIRS). In a randomized, single-blind, sham-controlled design, 45 healthy participants received either dual-target stimulation, single-target stimulation, or sham stimulation, before and after which working memory performance was performed using n-back tasks with simultaneous fNIRS recording. Behavioral results showed that dual-target stimulation improved working memory performance, particularly under higher cognitive load, and yielded a greater proportion of responders when compared with sham stimulation, whereas single-target stimulation induced relatively limited effects. fNIRS findings indicated that dual-target stimulation modulated task-related cortical activation in prefrontal and temporal regions and enhanced both intra- and inter-hemispheric functional connectivity. Moreover, improvements in task accuracy were associated with strengthened functional connectivity within the frontoparietal and default mode networks. These findings suggest that dual-target tACS may be a promising strategy for enhancing working memory and highlight the importance of network-level modulation in cognitive enhancement.
6 Apr 2026
Read appraisal →Neuroscience
Limitations and opportunities in multi-omics integration for neurodevelopmental, neurodegenerative and psychiatric disorders: A systematic review
Recent advances in high-throughput technologies have led to an increased generation of biological data across genomics, transcriptomics, proteomics, epigenomics, and metabolomics. However, a major challenge remains: effectively integrating these multi-omics datasets to allow a more holistic understanding of the complex, interconnected mechanisms underlying human diseases. Neurodevelopmental, neurodegenerative, and psychiatric disorders are particularly multifactorial and heterogeneous, making them candidates for multi-omics approaches. In this context, this systematic review assesses the current state of multi-omics integration in neurological research. Records retrieved from five major databases were processed, and 156 studies were included for further analysis. The most frequently studied conditions were Alzheimer's Disease, Depressive Disorder and Parkinson's Disease, with epigenomics-transcriptomics and metagenomics-metabolomics emerging as the most common omics pairings. The field remains dominated by studies integrating pairs of omics layers. Only a limited number of computational tools are currently being applied to the integration of more than two omics layers, highlighting a gap in comprehensive multi-omics modeling. Despite progress, key challenges persist, including data accessibility and the need for standardized frameworks to allow cross-study comparisons. Moreover, most computational findings lack experimental validation in wet-laboratory settings. Future research should address these challenges, develop scalable algorithms for integrating multi-omics data, and leverage large, open-access datasets. Integrating computational predictions with experimental validation could help researchers prioritize high-confidence biomarkers relevant to clinical applications. Collaborative efforts among bioinformaticians, clinicians, and experimentalists will be essential to translating these advances into clinically actionable solutions.
6 Apr 2026
Read appraisal →European journal of cardiovascular nursing
Impact of home-based cardiac rehabilitation in people with heart failure with elevated depressive and anxiety symptoms: a secondary analysis of the REACH-HF randomized trials
AIMS: This study examined whether elevated baseline depression and anxiety moderated the effects of the REACH-HF home-based cardiac rehabilitation (CR) programme on patient outcomes in individuals with heart failure. METHODS AND RESULTS: Individual patient data from 263 patients with HF from the REACH-HF HFrEF and HFpEF trials were analysed. At baseline, 62 (24%) had elevated depression [Hospital Anxiety and Depression Scale (HADS) D > 7], and 86 (33%) had elevated anxiety (HADS-A > 7). Patients were randomized to home-based CR plus usual care or usual care alone. Outcomes at 6-12 months included depression and anxiety (HADS), health-related quality of life (HRQoL) [Minnesota Living with Heart Failure Questionnaire (MLHFQ), HeartQoL], and accelerometer-measured physical activity (PA). Linear regression models, adjusting for baseline values, stratification variables, and confounders, included interaction terms (baseline depression/anxiety ×intervention group) to assess moderation effects. Those with elevated depression showed greater benefits from CR effects in depressive symptoms and HRQoL, with significant interactions for HADS-D (P = 0.004), MLHFQ (P = 0.007), HeartQoL (P = 0.001). No effects were found for PA. In participants with elevated anxiety, significant interactions were observed for HADS-D (P = 0.024), HeartQoL physical (P = 0.04), inactivity (P = 0.02), and light PA (P = 0.04). No effects were found for anxiety symptoms or HRQoL measures. CONCLUSION: People with HF participating in REACH-HF with elevated depression and anxiety experienced greater improvements in their mental health and HRQoL than individuals without elevated symptoms. Diagnostic clinical interviews in future studies would allow for stronger conclusions regarding the impact of home-based CR on patients with confirmed depression or anxiety.
5 Apr 2026
Read appraisal →The pharmacogenomics journal
Economic effectiveness of pharmacogenomics-guided prescribing for psychiatric disorders: a systematic review and meta-analysis
Pharmacogenomics (PGx)-guided prescribing is a promising approach to reduce variability in drug response, although its cost-effectiveness remains uncertain. We performed a systematic review and meta-analysis evaluating the cost-effectiveness of PGx-guided prescribing compared to standard care in psychiatry. In January 2026, we searched MEDLINE, Embase and PsycINFO for studies published between 2014 and 2025. We included any peer-reviewed study that included adults with a diagnosed psychiatric disorder, comparing PGx-guided prescribing to standard care, and reported both quality-of-life and economic outcomes. Given the lack of consensus on synthesising economic evidence, both a narrative synthesis and meta-analysis were conducted. Pooled incremental net benefit (INB) was used as the effect measure for the meta-analysis and heterogeneity measures including the I2 test were used to assess heterogeneity and determine which model to use for the meta-analysis. From an initial 1 271 records, 17 studies were included. The narrative synthesis found that 88% of studies favoured PGx-guided prescribing. Meta-analyses produced positive, though non-significant, pooled Incremental Net Benefits (INBs) for the total study groups (£1 623.14, 95% CI: -£116.50 to £3 362.79, p = 0.07, I2 = 100%), and for a statistically homogeneous subgroup (£41.54, 95% CI: -£18.27 to £101.35, p = 0.17, I2 = 0%). Our review indicates that PGx-guided prescribing can be cost-effective in psychiatry but highlights the need for increased consensus in economic modelling methods.
5 Apr 2026
Read appraisal →BMJ global health
Risk and protective factors of depression among caregivers of children with disabilities in rural Rwanda
BACKGROUND: Caregivers of children with disabilities are at increased risk of experiencing poor mental health due to the compound effects of financial and family stressors. This study aims to identify risk and protective factors for poor mental health, particularly depression, among caregivers of young children with disabilities. METHODS: This is a secondary data analysis, including a total of 1518 caregivers who were enrolled at baseline in a cluster randomised trial evaluating the effects of Sugira Muryango, a home-visiting parenting intervention, on early child development and violence prevention outcomes. A total of 309 primary caregivers were identified as having a child aged 12-36 months who screened positive for some form of developmental delay. Associations between key sociodemographic and household characteristics and depression outcomes were explored using multivariable linear regression models. RESULTS: Among the sample of caregivers of children with disabilities, 65% met the clinical cut-off for likely depression. Economic hardship (β=0.158, p<0.001), food insecurity (β=0.009, p<0.01) and caregiver illness (β=0.183, p<0.01) were significant risk factors for depression. In contrast, family unity (β=-0.013, p<0.001) was an important protective factor for depression among these caregivers. Among married/cohabiting caregivers, intimate partner violence (IPV) was associated with higher mean depression scores (β=0.306, p<0.001), though the inclusion of family unity in the full model (inclusive of risk and protective factors) attenuated the impact of IPV on depression. CONCLUSIONS: Programmes targeting children with disabilities should also address the mental health needs of parents. Mental health difficulties are common for caregivers living in poverty, especially those raising a child with a disability or developmental delay. Additionally, parenting programmes that reduce family violence and build family unity should be prioritised by policymakers interested in improving the mental health and well-being of families facing extreme poverty.
5 Apr 2026
Read appraisal →PloS one
Patterns and predictors of variability in patient-generated daily pain severity collected via a mobile health smartphone app.
Digital-health technologies support the collection of patient-generated health data that is frequent, longitudinal, and collected in participant's own environments. Such high-frequency data could detect patterns of variation in disease and associated symptoms, but characterizing, interpreting, and understanding the reasons for this variability remain open questions. Here, we examine 2070 people living with chronic pain to quantify daily variability in pain severity across seven-day periods and identify factors associated with that variability. Data were collected via a smartphone application from a population-based mobile-health study, Cloudy with a Chance of Pain. Summary statistics and distributions of pain changes on consecutive days were calculated within 13,052 complete weeks of data, which had been assigned to one of four clusters via a previously published k-mediods clustering algorithm: no/low pain, mild pain, moderate pain, and severe pain. Cumulative-probit models were used to identify associations between changes in pain severity and changes in exposure data. Across the four clusters, the no/low-pain cluster had the highest proportion of weeks with no within-week changes (59%) in pain severity compared to the other clusters (48-53%). When pain did change, it changed one unit (out of five) about 20% of the time, but larger changes of two to four units also occurred. Changes in pain severity were associated most strongly with changes in pain interference (i.e., how pain impacts daily activities) and were also associated with changes in fatigue, morning stiffness, mood, and participant well-being. Thus, this study showed that data collected frequently through digital-health technologies can be used to explore variability in symptoms and their associations with other variables. That pain severity was associated with changes in modifiable variables (e.g., fatigue, mood) suggests opportunities for different treatment and self-management regimes for different patient subtypes within the four clusters.
5 Apr 2026
Read appraisal →Journal of behavioral addictions
When rejection backfires: Social exclusion and economic decision-making in gamblers
BACKGROUND AND AIMS: Economic decision-making deviates from rational choice models and is influenced by social factors, particularly fairness considerations and belonging needs. Social exclusion and altruistic punishment are intended to promote behavioral conformity, yet paradoxically may exacerbate rather than correct targeted behaviors among individuals with addictions already experiencing chronic rejection. Several preliminary studies show that social exclusion increases money desirability in general populations, however, the interaction between acute social exclusion and economic decision-making among individuals with gambling disorders (PGs)-marked by aberrant financial decision-making-remains unexplored. METHODS: In a field study, 203 gamblers were recruited outside gambling venues and randomized to experience either social inclusion or exclusion via Cyberball, a virtual ball-tossing paradigm. Participants then completed an ultimatum game, making accept/reject decisions on fair and unfair monetary offers, followed by the Problem Gambling Severity Index. RESULTS: Social exclusion increased unfair offer acceptance among PGs but had no effect on low- and moderate-risk gamblers. PGs accepted more unfair offers following exclusion than inclusion. Gambling severity had no effect on unfair offers acceptance. Neither social exclusion nor gambling severity affected fair offer acceptance. DISCUSSION AND CONCLUSIONS: Social exclusion triggered utilitarian decision-making only among PGs. This selective effect suggests rejection compromises PGs' economic decision-making processes, shifting priorities toward immediate monetary gains over equitable outcomes. These findings reveal how social punishment creates self-reinforcing cycles where exclusion triggers the utilitarian mindset characterizing problematic gambling. Treatment and societal approaches relying on social consequences may inadvertently strengthen harmful patterns, highlighting the need for inclusion-based interventions.
5 Apr 2026
Read appraisal →BMJ paediatrics open
Mapping the global prevalence and socioecological drivers of child sexual abuse: a systematic review and synthesis
INTRODUCTION: Child sexual abuse (CSA) is a widespread global health concern with lasting impacts. CSA is multifactorial and layered, and interventions to tackle CSA require an analysis of the key contributing factors. The main objective of this study was to explore global prevalence estimates of CSA and key contributing factors. METHODS: We systematically searched PubMed, Scopus and Web of Science for studies reporting lifetime prevalence of contact (eg, inappropriate touching or sexual acts) and non-contact (eg, exposure to sexual content or ill-intended conversations) CSA. Of 5493 articles screened, 332 met inclusion criteria. Prevalence data were stratified by population, region and abuse type. Risk factors were synthesised thematically using a socioecological framework. RESULTS: Median prevalence of reported non-contact CSA was 57.0% (range: 31.1%-80.0%). Contact CSA prevalence was higher among females (median: 14.9%, range: 2.2%-40.2%) than males (median: 6.0%, range: 0.6%-21.2%). Regional variations were notable, with sub-Saharan Africa reporting the highest contact CSA prevalence (median: 25.5%) and Latin America and the Caribbean the lowest (median: 8.5%). Vulnerable groups, including Indigenous children (median: 39.2%), orphans (37.9%) and the incarcerated (31.1%), faced higher risk. Risk factors spanned individual (shame, stigma), familial (poor supervision, dysfunction), institutional (lack of safeguards) and societal (weak legal frameworks, normalisation of abuse) levels. CONCLUSIONS: Prevalence of CSA was globally high, with 14 in 100 females reporting having experienced CSA. Multilevel interventions, particularly strengthening family supervision and institutional safeguarding, legal accountability and community awareness, are critical for the prevention of CSA.
4 Apr 2026
Read appraisal →Sexual medicine reviews
Internet-based cognitive behavioral therapy, web-based counseling, and online/digital psychoeducation for male sexual dysfunction: a systematic review
INTRODUCTION: Male sexual dysfunctionhas a substantial impact on psychological well-being and intimate partner relationships. The expanding availability of digital health services has enabled online interventions, such as internet-based cognitive behavioral therapy (ICBT), digital psychoeducation, and web-based counseling, to emerge as alternatives to conventional therapies. OBJECTIVE: To evaluate the effectiveness, feasibility, and acceptability of digital and online interventions for male sexual dysfunction, and to synthesize evidence from randomized, non-randomized, and observational studies on clinical outcomes and patterns of digital engagement. METHODS: This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A comprehensive literature search was conducted across several databases using predefined keywords. Eligible studies included randomized controlled trials, experimental studies, and analytical observational studies published between 2007 and 2024. Extracted data comprised country, participant characteristics, type and duration of intervention, control conditions, outcome measures, and main findings. The primary outcome was improvement in erectile/sexual function using certain parameters, while secondary outcomes included relationship satisfaction, anxiety, and depression. RESULTS: Of 2482 records identified, and 21 met the inclusion criteria. Overall, web-based ICBT and psychoeducational interventions improved erectile function and sexual satisfaction compared with control conditions. Most interventions lasted 6-12 weeks and incorporated therapist feedback or automated systems. Remission rates for erectile dysfunction ranged from 20% to 30%; however, participant adherence was low (<60%). Several studies also reported reductions in performance anxiety and improvements in sexual confidence, with no serious adverse effects. CONCLUSION: Online interventions are promising approaches for managing male sexual dysfunction. Nevertheless, low adherence rates and heterogeneity in intervention formats remain major challenges. The findings also suggest heterogeneous benefits of digital interventions, alongside improved accessibility, reduced stigma, and increased engagement in help-seeking among men with sexual dysfunction. Future research should prioritize strategies to enhance adherence, integrate mobile health technologies, and assess long-term outcomes through large-scale clinical trials.
4 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 →BMJ open
Utilisation of brain MRI for the diagnosis of dementia in low and middle-income countries: systematic review and meta-analysis
OBJECTIVES: To synthesise the prevalence and patterns of dementia-relevant structural brain MRI abnormalities in adults with suspected or confirmed dementia in low- and middle-income countries (LMICs), and to summarise MRI protocols and the incremental diagnostic contribution of MRI beyond cognitive screening. DESIGN: Systematic review and meta-analysis. DATA SOURCES: PubMed, EMBASE, Web of Science and PsycINFO (January 1990-27 January 2025), plus reference list screening and targeted manual searches. ELIGIBILITY CRITERIA: Observational or diagnostic-accuracy studies from World Bank-defined LMICs including adults (≥50 years) with suspected or confirmed dementia who underwent brain MRI as part of diagnostic evaluation. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently screened, extracted data and assessed risk of bias using ROBINS-I. Random-effects models pooled prevalence of dementia-relevant MRI abnormalities; diagnostic-accuracy outcomes were synthesised narratively due to heterogeneous reference standards and incomplete reporting. RESULTS: 39 LMIC studies were included; 23 studies (2513 participants) contributed to the meta-analysis. Dementia-relevant MRI abnormalities (defined as ≥1 clinically relevant structural abnormality per study definition) were present in 1248/2513 participants. The pooled prevalence of dementia-relevant MRI abnormalities was 58% (95% CI 43% to 72%), with substantial heterogeneity (I²=95%) and a wide prediction interval (8-96%), indicating marked between-study variability; this estimate should be interpreted as a descriptive summary of study-level proportions rather than a precise population parameter. CONCLUSIONS: Brain MRI frequently demonstrates dementia-relevant pathology in LMIC clinical cohorts, usually with mixed neurodegenerative-vascular patterns. Structured visual ratings may add aetiologic specificity beyond cognitive screening, but pooled estimates should be interpreted as summaries of heterogeneous study-level findings rather than precise population parameters, given high heterogeneity and risk of bias. PROSPERO REGISTRATION NUMBER: CRD42024510241.
4 Apr 2026
Read appraisal →Journal of medical Internet research
The Role of Digital Biomarkers in Physiological Signal-Based Depression Assessment: Systematic Review and Meta-Analysis
BACKGROUND: Digital biomarkers are increasingly being used to support depression assessment by providing objective, continuous, and real-time physiological and behavioral data. However, most existing studies have focused on individual biomarkers, such as sleep or cardiac parameters, while integrative evaluations that capture the multidimensional nature of depression remain limited. OBJECTIVE: This systematic review evaluated digital biomarkers for depression and synthesized evidence on differences between individuals with depression and controls. METHODS: Eligible studies included observational or interventional studies examining digital biomarkers for depression with validated outcome measures. We searched major international and Korean databases, including MEDLINE, PsycINFO, CINAHL, IEEE Xplore, Web of Science, Cochrane Library, KISS, RISS, KMbase, and KoreaMed, from inception to December 28, 2025. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool and the Scottish Intercollegiate Guidelines Network checklist. Meta-analyses were conducted using random-effects models with the Hartung-Knapp-Sidik-Jonkman method, and other outcomes were narratively summarized. RESULTS: The search yielded 39,617 records, of which 132 studies involving 57,852 participants met the inclusion criteria. These studies encompassed various digital biomarkers, including sleep, physical activity, cardiac measures, smartphone-derived data, speech, GPS data, and circadian rhythms. A meta-analysis of 22 studies (6947 participants) revealed that individuals with depression had significantly longer sleep onset latency (5 studies; n=292; +4.75 min, 95% CI 2.46-7.04; P=.005; 95% prediction interval [PI] 0.01-10.27) and time in bed (3 studies; n=236; +31.81 min, 95% CI 18.22-45.39; P=.01; 95% PI 2.28-55.16). Physical activity counts were also significantly lower (5 studies; n=462; standardized mean difference -0.71, 95% CI -1.33 to -0.09; P=.03; 95% PI -2.18 to 0.71). Although individuals with depression showed a lower sleep efficiency, higher mean heart rate, and lower SD of normal-to-normal intervals, these differences were not statistically significant. Other digital markers yielded inconsistent results. Overall, these findings indicate that no single digital biomarker sufficiently captures depression-related changes. Instead, the results support the superiority of personalized, multimodal approaches. However, the generalizability of these findings is limited by the lack of standardized data collection protocols and high clinical heterogeneity across studies, as reflected in wide PIs. CONCLUSIONS: Certain digital biomarkers, particularly sleep onset latency and physical activity counts, showed consistent average differences between the depression and control groups. However, wide PIs indicate substantial variability across settings, suggesting that no single marker is sufficient for reliable detection. This study advances the field by providing a comprehensive meta-analysis of multidimensional digital biomarkers, establishing a quantitative foundation for objective depression screening and monitoring. These findings support the use of personalized, multimodal digital phenotyping approaches and highlight the need for standardized, clinically interpretable frameworks for real-world depression monitoring.
4 Apr 2026
Read appraisal →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 →JMIR research protocols
Development and Validation of Machine Learning Models for Predicting Early Cognitive Decline Using Home Sensor-Derived Behavioral Data: Sensors in-Home for Elder Wellbeing (SINEW) Cohort Study
BACKGROUND: As the global population continues to age, the prevalence of geriatric conditions, including dementia and frailty, is also increasing. Early identification of individuals at an elevated risk of these conditions, such as those presenting with mild cognitive impairment (MCI) or prefrailty, can provide a critical window for prompt intervention aimed at preventing or reversing disease progression. To promote such early identification, there is a burgeoning interest in the use of digital sensor technology and predictive modeling. OBJECTIVE: This study aimed to use a continuous, home-based monitoring sensor system for older adults to distinguish those exhibiting normal aging from those with MCI, early dementia, prefrailty, or frailty, and to predict their transition from normal aging to one of these conditions. METHODS: This longitudinal cohort study will recruit 200 community-dwelling adults aged ≥65 years with normal cognition or MCI at baseline. A multi-sensor system will be installed in participants' homes, including passive infrared motion sensors, door contact sensors, bed sensors, medication box sensors, wearable activity bands, and Bluetooth proximity beacons. These devices will continuously capture spatiotemporal activity patterns, mobility indicators, sleep behaviors, and medication-taking routines. Annual assessments will include standardized cognitive tests (eg, Montreal Cognitive Assessment, Mini-Mental State Examination, Rey Auditory-Verbal Learning Test, digit span, Color Trails Test, semantic fluency, Stroop), frailty measures (modified Fried phenotype, gait speed, grip strength), mental health scales, sleep quality, and psychosocial indicators. Sensor-derived features-such as gait variability, activity regularity, sleep fragmentation, and medication adherence patterns-will be integrated with clinical data to develop supervised machine learning models. Planned approaches include logistic regression, random forests, gradient boosting, and deep learning. Model performance will be evaluated using cross-validation and independent test sets. Primary metrics will include area under the receiver operating characteristic curve, sensitivity, specificity, precision, recall, and F1-score. Models will be benchmarked against gold-standard clinical diagnoses and validated using temporal subsets of the dataset. RESULTS: Enrollment for this study started in November 2019 and will continue until March 2030. As of June 2025, we have enrolled 138 participants. Full data analysis has yet to begin. CONCLUSIONS: We aim to develop a reliable and effective sensor system for in-home use that will facilitate the early detection of cognitive and physical decline. In so doing, it will add to our current understanding of digital biomarkers. It is common for older adults to seek clinical intervention only when their cognitive impairment has already reached an advanced stage. The implementation of readily deployable sensor systems within community settings presents us with opportunities for prompt intervention, which holds the potential for delaying or reversing disease progression and allowing for a greater number of functional and meaningful years.
4 Apr 2026
Read appraisal →Current opinion in genetics & development
Deep learning for psychiatric genomics: from tools to applications
The genetic architecture of psychiatric disorders is highly complex, with genome-wide association studies implicating thousands of risk loci. A central challenge is that most of these variants are located in noncoding regions, making it difficult to elucidate their regulatory consequences within the brain's intricate cellular landscape. The recent convergence of advanced artificial intelligence, particularly deep learning (DL), has catalyzed a paradigm shift by providing powerful tools to address this gap. This review traces the evolution of DL in genomics, beginning with task-specific models. We then examine the transformative impact of foundation models, pretrained neural networks that learn the 'language' of biology, including genomic language models, single-cell foundation models, and large language models originally trained on natural language. Finally, we survey applications to key problems in psychiatric genomics. We hope this review provides a comprehensive overview of recent advances in DL for genomics and serves as a bridge to help researchers in psychiatric genomics more effectively understand and apply these frontier methods to guide the development of novel therapeutic strategies for psychiatric disorders.
4 Apr 2026
Read appraisal →Transl Psychiatry
Application of artificial intelligence in schizophrenia rehabilitation management: a systematic scoping review
Artificial intelligence (AI) is increasingly used in mental health, yet its rehabilitation-oriented applications in schizophrenia have not been systematically mapped. We conducted a systematic scoping review of PubMed, Web of Science, IEEE Xplore and the ACM Digital Library (January 1, 2012-October 31, 2025; two search rounds), applying operationalized rehabilitation boundaries and excluding diagnostics-only case-control studies. We extracted data on data sources, feature engineering, model families, validation, calibration, interpretability, application domains, outcomes and implementation readiness. Eighty-three studies met inclusion criteria (median sample size 160; 55% longitudinal). Applications focused on symptom monitoring (48/83), medication management (19/83) and risk management (16/83), whereas functional training (1/83) and psychosocial support (3/83) were rarely targeted. Supervised learning predominated (53/83, 63.9%) over representation learning (20/83, 24%), most commonly using speech/text, electronic health records and smartphone sensing. Across classification tasks, the median AUC was 0.79 (IQR 0.71-0.86); relapse early-warning models showed a median sensitivity of 31.5% at 88.0% specificity. Only four studies reported external validation and three described closed-loop deployment, including one randomized trial that improved adherence. Proxy endpoints were more common than clinical endpoints, and reporting of calibration/uncertainty and fairness auditing was sparse. Overall, AI shows promise for monitoring, adherence support and relapse risk stratification, but routine-care deployment will require externally validated and calibrated human-in-the-loop decision support, privacy-preserving multimodal pipelines and pragmatic trials targeting functional outcomes and participation.
1 Mar 2026
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
Read appraisal →Nat Hum Behav
Machine learning-assisted optimization of dietary intervention against dementia risk
A healthy diet has been associated with a reduced risk of dementia. Here we devised a Machine learning-assisted Optimizing Dietary intERvention against demeNtia risk (MODERN) diet based on data from 185,012 UK Biobank participants, 1,987 of whom developed all-cause dementia over 10 years. We first identified 25 food groups associated with dementia in a food-wide association analysis. Second, we ranked their importance using machine learning and prioritized eight groups (for example, green leafy vegetables, berries and citrus fruits). Finally, we established and externally validated a MODERN score (0-7), which showed stronger associations with lower risk of dementia-related outcomes (hazard ratio comparing highest versus lowest tertiles: 0.64, 95% CI: 0.43-0.93) than the a priori-defined MIND diet (0.75, 0.61-0.92). Across 63 health-related outcomes, the MODERN diet showed particularly significant associations with mental/behavioural disorders. Multimodal neuroimaging, metabolomics, inflammation and proteomics analyses revealed potential pathways and further support the potential of MODERN diet for dementia prevention.
1 Nov 2025
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