News Approved – Page 51

May 13, 2026

I’m scared of everything — what does it mean and how do I get over it?

What you’re describing sounds really overwhelming. I’m glad you reached out. The fears you mention — being scared of doing something against your will, worrying you might not have control, and feeling intensely concerned about being judged — are patterns I often see in people with anxiety and, sometimes, people with obsessive-compulsive disorder (OCD). A hallmark of OCD is a deep doubt about control: the fear that you might act in a way that goes against your values, even though you don’t want to. These kinds of fears are called intrusive thoughts. While intrusive thoughts can feel very real and frightening, they are not things you actually intend to do or predictions of things that you will do — they’re unwanted experiences that don’t define you.

Avoiding sports and other things for fear of being judged is also a symptom of anxiety. I can understand how hard it is to tell your family what you’re going through, especially if you have felt ignored in the past. At the same time, your pain deserves to be heard and taken seriously. I encourage you to try talking to your parents again, but if you truly feel like you can’t, consider telling one safe person — whether that’s another family member, a school counselor, or even a teacher you trust. You can write how you’re feeling in a note if speaking feels too hard.

The physical symptoms you mentioned — neck and shoulder pain, fidgeting — are also common in anxiety because our bodies can hold tension when our brains are on high alert. What this likely means is that your brain is caught in a fear loop, constantly scanning for danger around control and judgment.

The good news is that this is very treatable. A mental health professional may recommend a type of cognitive behavioral therapy called exposure and response prevention (ERP). ERP helps you gradually face the situations or thoughts you fear instead of looking for reassurance from someone else or avoiding those situations or thoughts altogether. Over time, ERP teaches your brain that thoughts are just thoughts, not actions, and that you can tolerate uncertainty without something bad happening.

For now, you might try gently labeling upsetting thoughts as anxiety, not facts, and practicing not accepting them as true when they show up. Taking small steps toward what you’ve been avoiding can help you rebuild your confidence, even if it feels uncomfortable at first.

While you can practice managing anxiety or intrusive thoughts on your own, it’s better to have help. Once you talk to someone you know and trust, have them help you reach out to a mental health professional who can provide a more thorough assessment and the appropriate treatment for you. You don’t have to go through this alone, and with the right support, this can get much better.

The post I’m scared of everything — what does it mean and how do I get over it? appeared first on Child Mind Institute.

May 13, 2026

Equitable Digital Frailty Screening for Marginalized Older Adults Using Audio Computer-Assisted Self-Interview: Collaborative Development Guide and User Testing Study

Background: Older adults facing social or structural marginalization for reasons such as lower literacy, digital exclusion, financial constraints, restricted living environments, or complex health histories, face persistent barriers to much-needed health screening. Digital health tools, particularly those using audio computer-assisted self-interview (ACASI) technology, offer potential to overcome these barriers (audio-delivered and self-administrable), but their application to marginalized populations remains underexplored. Moreover, guidance is limited for developing such tools which require collaboration within cross-disciplinary teams. This paper presents development insights and user testing findings from the ASCAPE (Audio App-Delivered Screening for Cognition and Age-Related Health in Prisoners) project, which aimed to develop equitable digital frailty and cognition screening for older people in Australian prisons. Objective: This study aims to describe the collaborative development of the “ASCAPE-HS” prototype, a tablet-based ACASI-delivered Frailty Index and aging screener, and to synthesize key lessons from the project that can inform equitable digital health tool development in hard-to-reach older adults. Also, to present findings on the usability and acceptability of ASCAPE-HS in a diverse community sample. Methods: The ASCAPE-HS prototype was developed through an iterative process involving researchers, clinicians, software developers, and end users under a digital health equity framework. The prototype included a self-administered, audio-delivered Frailty Index, alongside other items relevant to aging. The design process prioritized accessibility, sociocultural relevance, and technical feasibility, with regular multidisciplinary consultation and iterative refinement. Exploratory user testing with 20 older adults (aged 47‐93 years, including n=5 who had not finished secondary schooling, n=3 people with previous imprisonment history, and n=9 with mild or moderate cognitive impairment) provided feedback on usability and acceptability. Results: A 50-item Frailty Index was developed, alongside an additional selection of holistic questions that could meaningfully capture age-related health, and transferred to an iOS app (Apple, Inc), with ACASI features. Key elements included lay wording, consistent interface, simple “tapping” response options with repeatable audio feedback, a tutorial, and artificial intelligence–generated audio guidance. Key development considerations were synthesized into a checklist for teams undertaking similar projects. Successful strategies for the collaborative design process included diverse teams abreast of emerging literature and policy with varying expectations for engagement during development, and dedicating time to flexible, iterative development processes. Acceptability (median scores ≥4 out of 5 across 6 constructs) and usability (mean System Usability Scale score 79.0, SD 8.8) were high. Conclusions: A collaborative approach can produce ACASI-based health screening tools that are well-received by older adults. We highlight the feasibility of integrating frailty and aging assessment into a usable and acceptable digital tool, and offer actionable principles for collaborative, evidence-based development of equitable health screening tools in diverse, hard-to-reach populations.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/30699dc5ac2d0ad05f7755da3210033e" />

May 13, 2026

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.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/a59ed16526360bac492b29bed9672c02" />

May 13, 2026

Early Detection of Alzheimer’s Disease and Related Dementias From Spontaneous Speech Using Foundation Speech and Language Models: Comparative Evaluation

Background: Alzheimer’s disease and related dementias (ADRD) are progressive neurodegenerative conditions where early detection is critical for timely intervention and care planning. However, current diagnostic methods are often inaccessible, costly, and delayed, especially for underserved populations. There is a growing need for scalable, noninvasive tools that can support timely diagnosis. Spontaneous speech contains rich acoustic and linguistic markers that can serve as noninvasive behavioral markers for cognitive decline. Foundation models, pretrained on large-scale audio or text data, generate high-dimensional embeddings that encode rich contextual and acoustic information. Objective: This study benchmarks open-source foundation language and speech models to evaluate their effectiveness in detecting ADRD from spontaneous speech as a potential solution for early, noninvasive, and scalable ADRD detection. Methods: In this study, we used the Pioneering Research for Early Prediction of Alzheimer’s and Related Dementias EUREKA (PREPARE) Challenge dataset, which consists of audio recordings from over 1600 participants with 3 distinct categories of cognitive decline: healthy control (HC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD). We further excluded samples that are non-English, nonspontaneous speech, or of poor quality. Our final samples included 703 (59.13%) HC, 81 (6.81%) MCI, and 405 (34.06%) AD cases. We systematically benchmarked 18 open-source foundation speech and language models to classify cognitive status into 3 categories (HC, MCI, or AD). Post hoc interpretability analysis was performed for the best-performing model using Shapley additive explanations linking high-dimensional embeddings with explainable acoustic and linguistic markers. Results: Whisper-medium model achieved the highest performance among speech models at 0.731 accuracy and 0.802 area under the curve, while Bidirectional Encoder Representations from Transformers with pause annotation achieved the top accuracy of 0.662 and 0.744 area under the curve among language models. Overall, ADRD detection based on state-of-the-art automatic speech recognition model-generated audio-embeddings outperformed other models, and the inclusion of nonsemantic information, such as pause patterns, consistently improved the classification performance of text-embedding–based models. Conclusions: Our work presents a comprehensive comparative evaluation of state-of-the-art speech and language models for AD and MCI detection on a large, clinically relevant dataset. Embeddings derived from acoustic models, which capture both semantic and acoustic information, show promising performance and highlight the potential for developing a more scalable, noninvasive, and cost-effective early detection tool for ADRD.

May 13, 2026

iCARE Self-Guided Digital Intervention for Postpartum Depression in Danish Mothers: Formative Research Using User-Centered Design

Background: Postpartum depression (PPD) is a major public health concern. Despite advancements in treatment, many barriers to accessing care remain. There has been a growing interest in digital interventions for the prevention and treatment of PPD. However, for mothers with mild and moderate symptoms of depression, there is a limited offer of self-guided internet-based interventions developed with user input and with considerations on how to integrate the intervention into stepped care models for PPD. Objective: The aim of this study was (1) to describe the process of the design and development of iCARE, a self-guided digital psychological intervention for mothers with mild and moderate symptoms of PPD in Denmark, (2) present the program’s theory illustrated by a logic model, and (3) explore its initial usability and prospective acceptability. Methods: Applying user-centered design methods, the intervention development followed six steps: (1) a literature review to identify evidence‑based therapeutic components of self‑guided interventions for PPD, (2) interviews with women with lived experience of PPD and group discussions with mental health experts and home‑visiting providers to identify user needs, (3) iterative design and content development with stakeholder feedback in collaboration with the Department of Digital Psychiatry, (4) prototype testing using think‑aloud usability sessions and interviews with 5 mothers, (5) a group cognitive walkthrough with mental health experts, and (6) final refinement and implementation of the iCARE program with developers and designers. Results: Initial interviews with mothers and maternal health care providers emphasized the importance of a digital intervention offering timely psychoeducation, coping strategies, and pathways to in-person care while addressing the diversity of expressions of PPD symptoms. Stakeholders recommended a flexible program, multimodal content, and integration into maternal care systems with community health nurses supporting engagement and participation. The prototype was designed to be user-centered, engaging, and with multiple interactive features. It included components on psychoeducation, cognitive exercises grounded in cognitive behavioral therapy, acceptance and commitment principles, and mood-monitoring. The prototype was designed to be user-centered and engaging, with interactive features and components on psychoeducation, cognitive exercises grounded in cognitive behavioral and acceptance and commitment principles, and mood-monitoring. Prototype testing indicated high prospective acceptability and led to refinements across 6 themes: appropriateness of content; motivation and engagement; inclusivity and gender representation; clarity of instructions and data use; understanding of therapeutic method; and usability, layout, and navigation. Conclusions: iCARE is a self-guided internet-based psychological intervention for mothers with mild and moderate symptoms of PPD in Denmark. It was developed with user input by using qualitative methods, user-centered design, and psychological theory. Further research is needed to evaluate the feasibility and effectiveness of the program in a randomized controlled trial and its integration into maternal health care models such as universal PPD screening and home-visiting.

May 13, 2026

Roles of NRXN1 in neuropsychiatric disorders: from genetic lesion to molecular mechanism

Numerous neuropsychiatric disorders frequently exhibit overlapping genetic risk factors, implying the molecular basis for their comorbidity. Nevertheless, the pathogenesis of these disorders remains elusive, particularly regarding how genetic variations impair the physiological function of risk genes and contribute to disease phenotypes. Neurexin 1 protein, encoded by NRXN1 gene, belongs to the neurexin family of presynaptic adhesion molecules. And neurexin 1 is involved in synaptogenesis and the maintenance of synaptic action. Genetic variations of NRXN1 have been demonstrated to be associated with a spectrum of neuropsychiatric disorders. Herein, this review focuses on the most recent and relevant literature concerning the genetic and molecular mechanisms through which NRXN1 variants contribute to the pathogenesis of neuropsychiatric disorders, particularly schizophrenia and autism spectrum disorder. Among them, we propose the isoform-dependent excitation-inhibition imbalance hypothesis of NRXN1 in autism spectrum disorder. And this hypothesis may account for both the elevated and decreased excitation-inhibition ratios observed in diverse individuals with autism spectrum disorder. Moreover, both schizophrenia and autism spectrum disorder involve deletions and alternative splicing of NRXN1, offering molecular evidence for their comorbidity. Then, we analyzed and summarized the current research status of NRXN1 in other neuropsychiatric disorders, including attention-deficit hyperactivity disorder, insomnia, epilepsy, suicide, and depression. Additionally, available limited researches on NRXN1-targeted therapeutic strategies and associated pharmacological studies are also incorporated. Finally, we discussed existing challenges in NRXN1 research within the context of neuropsychiatric disorders and proposed potential avenues to overcome these obstacles.

May 13, 2026

Direct modulation of human GABA-A α1β2γ2 receptors by the endocannabinoid 2-arachidonoylglycerol: implications for cannabinoid-related ligands and limitations for anxiolytic drug development

Anxiety disorders are associated with impaired inhibitory neurotransmission mediated by γ-aminobutyric acid type A (GABA-A) receptors. Although benzodiazepines remain effective anxiolytics, their clinical utility is limited by sedation, cognitive impairment, tolerance, and dependence, prompting the search for mechanistically distinct GABAergic modulators. Among cannabinoid-related molecules, the strongest evidence for direct GABA-A receptor modulation concerns the endocannabinoid 2-arachidonoylglycerol (2-AG), which potentiates recombinant human α1β2γ2 receptors through residues located in the M4 helix of the β2 subunit. Here, we review the structural architecture, biophysical properties, and pharmacological profile of the human GABA-A α1β2γ2 isoform as the relevant molecular framework for evaluating this mechanism, while discussing the broader relevance of cannabinoid-related ligands and selected phytocannabinoids without assuming mechanistic equivalence. We further assess the hypothesis that 2-AG reaches the β2-M4 site through a membrane-access route and identify five conceptual barriers that currently limit translation of this mechanism into anxiolytic drug development: supraphysiological effective concentrations, unresolved synaptic-versus-extrasynaptic actions, uncertain subtype selectivity, incomplete validation of lipid-environment effects, and lack of clinical evidence linking this mechanism to anxiolysis in humans. We conclude that direct modulation through β2-M4 defines a mechanistically intriguing allosteric pathway distinct from benzodiazepine action; however, its location on a shared β2 subunit and the micromolar concentrations required for modulation represent substantial obstacles to the rational design of anxioselective agents based on this mechanism.

May 13, 2026

Romanian male patients with the dual diagnosis of schizophrenia and alcohol use disorder: a prospective study of clinical, social, and treatment-related factors affecting quality of life

BackgroundSchizophrenia frequently co-occurs with alcohol use disorder (AUD), resulting in a complex clinical profile associated with poor functional outcomes and reduced quality of life (QoL). Although both conditions independently impair psychosocial functioning, few studies have examined the combined effects of clinical, social, and treatment-related factors on QoL in patients with this dual diagnosis.MethodsThis prospective observational study included 88 male inpatients diagnosed with schizophrenia and comorbid AUD and who were followed over a 6-month period. Quality of life was assessed using the World Health Organization Quality of Life–BREF (WHOQoL–BREF). The clinical variables included severity of psychotic symptoms (Positive and Negative Syndrome Scale), alcohol use severity (Michigan Alcohol Screening Test), and treatment characteristics. Social and personal factors, such as self-care capacity, social support, education, and legal problems, were also evaluated. Multivariable regression analyses were conducted to identify predictors of QoL at baseline and follow-up.ResultsAt baseline, higher QoL was significantly associated with greater self-care capacity, social support, and higher positive symptom scores, while the need for antipsychotic treatment was associated with lower QoL. At the 6-month follow-up, better QoL was predicted by greater self-care capacity, higher educational level, and receipt of anti-craving medication. By contrast, negative and general psychopathology, medico-legal problems, and the need for antidepressant treatment were associated with poorer QoL. Alcohol use severity, as measured by the MAST, was not independently associated with QoL at either timepoint.ConclusionsIn patients with schizophrenia and comorbid AUD, QoL is shaped by a complex interaction of clinical severity, functional capacity, and treatment-related factors. Beyond symptom control, interventions targeting self-care, social functioning, and integrated addiction treatment appear essential to improve long-term outcomes. These findings support the implementation of a multidimensional, recovery-oriented approach for the management of patients with the dual diagnosis.

May 13, 2026

Assessing directional connections between symptoms, cognition, insight, and real-life functioning in schizophrenia: a partial ancestor graphs analysis

IntroductionSchizophrenia is a severe chronic mental disorder causing significant global disability. Understanding the intricate relationships between symptoms, cognitive functions, and real-life outcomes is essential for developing effective interventions. Prior research, while informative, could not often determine the direction of the association between these illness-related factors. This study aimed to investigate the possible causal connections within the interrelationships of these variables. MethodsThis cross-sectional study included 215 clinically stable patients diagnosed with schizophrenia. Comprehensive assessments covered psychopathology, neurocognition, social cognition, metacognition, clinical insight, and real-life functioning. Causal relationships were explored using Partial Ancestral Graphs, a causal discovery framework that accounts for mediators and confounders. The Greedy Fast Causal Inference algorithm was employed with 1,000 bootstrap replications to assign edge orientations.ResultsA central neurocognitive–metacognitive–functional system of directed connection emerged: visual learning was linked to attention/vigilance and working memory. Working memory showed a direct relationship with metacognition, which, in turn, was connected to real-life functioning. Two partly independent contributions to real-life functioning were also identified: conceptual disorganization and experiential negative symptoms, which were directly related to expressive deficits. Positive symptoms, depressive symptoms, and social cognition occupied peripheral positions, showing no significant connection with other variables. Unawareness and misattribution of symptoms showed an indeterminate association disconnected from the main network.DiscussionThe findings show a set of directed associations that start with neurocognitive abilities, pass through working memory and metacognition, and terminate in real-life functioning. Independently, conceptual disorganization and expressive negative symptoms also exert direct influences. These directed systems of connections provide operational guidance for clinical practice, highlighting critical targets for interventions such as cognitive remediation focused on working memory, metacognitive therapies, and strategies addressing disorganization and avolition, all aimed at improving real-life outcomes in schizophrenia.

May 13, 2026

Internalizing and externalizing pathways to internet gaming disorder: the roles of anger and social anxiety

BackgroundInternet Gaming Disorder (IGD) represents a significant behavioral health concern, yet the roles of internalizing and externalizing psychological vulnerabilities in its development remain underexplored, particularly in Arabic-speaking populations.ObjectiveThis study examined anger and social anxiety as distinct externalizing and internalizing predictors of IGD severity in a Saudi Arabian community sample.MethodsA cross-sectional survey was administered to 303 participants (60.1% female; estimated mean age = 29.79 years, SD = 8.83) across five regions of Saudi Arabia. Participants completed the Internet Gaming Disorder Scale–Short Form (IGDS9-SF), a three-item Anger Screening Scale, and a two-item Social Anxiety screener. Hierarchical linear regression and structural equation modeling (SEM) were conducted to examine unique and incremental contributions of anger and social anxiety to IGD symptoms.ResultsAnger and social anxiety were strongly intercorrelated (r = .86, p <.001) but demonstrated divergent patterns in multivariate models. Hierarchical regression indicated that both predictors contributed unique variance when entered simultaneously, with anger positively and social anxiety negatively predicting IGD after controlling for shared variance. However, SEM clarified that only social anxiety significantly predicted latent IGD severity (β = .32, p = .027), whereas anger did not (β = .07, p = .68). The final model explained approximately 13% of variance in IGD symptoms.ConclusionsSocial anxiety was associated with IGD severity as a distinct internalizing correlate, consistent with avoidance-based coping and online social preference accounts. These preliminary, cross-sectional findings suggest that social anxiety warrants consideration in future IGD screening and research efforts in Arabic-speaking contexts.