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"In Their Own Words": A Qualitative Exploration of Lived Experience and Healthcare Professional Perspectives on Evaluating a Digital Intervention for Binge Eating.
OBJECTIVE: Eating disorders characterized by binge eating are prevalent yet under-recognized, limiting access to effective care. The digital, programme-led (self-help) version of Enhanced Cognitive Behavior Therapy (CBT-E) offers a potentially scalable treatment. This study gathered insights from individuals with lived experience of binge eating (LE) and healthcare professionals (HCPs) to inform the design of a randomized controlled trial evaluating the intervention's effectiveness and to support early-stage implementation planning. METHOD: Four focus groups were conducted with 20 participants (8 with LE, 12 HCPs). Discussions explored recruitment strategies, participant engagement, meaningful outcome measures, and barriers to implementation. Data were analyzed using thematic analysis. RESULTS: Two overarching themes were identified: (1) Reach People in Accessible and Supportive Ways, and (2) Be Open to Different Experiences of Progress. Participants emphasized inclusive recruitment and compassionate, hopeful messaging. Stigma and limited recognition of binge eating were cited as recruitment barriers in healthcare settings. Both groups recommended community and online platforms to enhance reach. Participants stressed the importance of outcomes beyond symptom reduction (e.g., emotional well-being) and qualitative methods to capture recovery narratives. Findings also highlighted implementation-relevant factors, including how interventions are framed and delivered, and how engagement can be optimized. DISCUSSION: Perspectives from individuals with LE and HCPs support a person-centred trial aligned with the needs of those experiencing binge eating and those providing care, while considering both evaluative and implementation priorities. Findings inform strategies to enhance reach and understanding of digital intervention outcomes, contributing to trial designs that are consistent with real-world care and meaningful to participants.
Sex-stratified genome-wide association meta-analysis of major depressive disorder.
There are striking sex differences in the prevalence and symptomology of Major Depressive Disorder. Here, we conduct the largest sex-stratified genome wide association and genotype-by-sex interaction meta-analyses of Major Depressive Disorder to date (Females: 130,471 cases, 159,521 controls. Males: 64,805 cases, 132,185 controls). We identify 16 and eight independent genome-wide significant variants in females and males, respectively, including one novel variant on the X chromosome. Major Depressive Disorder in females and males shows substantial genetic overlap with a large proportion of variants displaying similar effect sizes across sexes. However, we also provide evidence for a higher burden of genetic risk in females which could be due to female-specific variants. Additionally, sex-specific pleiotropic effects may contribute to the higher prevalence of metabolic symptoms in females with Major Depressive Disorder. These findings underscore the importance of considering sex-specific genetic architectures in the study of health conditions, including Major Depressive Disorder, paving the way for more targeted treatment strategies.
Dopamine and Mood in Psychotic Disorders: An 18F-DOPA PET Study.
IMPORTANCE: There is limited neurobiological or trial evidence guiding treatment of comorbid affective syndromes in psychotic disorders. Given the use of dopamine-blocking antipsychotics, understanding dopamine function across these mood states is warranted. OBJECTIVE: To test for differences in dopamine synthesis capacity (Kicer) between affective syndromes across psychotic disorders and for association with psychotic symptom severity. DESIGN, SETTING, AND PARTICIPANTS: In this cross-sectional study using fluorine F 18-labeled fluorodopa (18F-DOPA) positron emission tomography (PET), individuals with first-episode psychosis and comorbid affective syndromes, including a current major depressive episode (MDE) or mixed/mania syndromes, and matched controls were recruited from early intervention services in inner-city London, United Kingdom. Data were collected from March 2013 to February 2022 and analyzed from October 1, 2023, to January 1, 2025. EXPOSURE: Striatal Kicer measured by 18F-DOPA PET. MAIN OUTCOMES AND MEASURES: Striatal Kicer and scores on the Positive and Negative Syndrome Scale, Hamilton Depression Rating Scale, Montgomery-Åsberg Depression Rating Scale, and Young Mania Rating Scale were determined. RESULTS: The study included a total of 76 individuals (38 with first-episode psychosis and comorbid affective syndromes [25 with MDE and 13 with mixed/mania syndromes] and 38 matched controls). The mean (SD) age was 27.2 (8.9) years overall, 30.7 (12.8) years among those with MDE, 23.7 (3.1) years among those with mixed/mania syndromes, and 26.0 (6.0) years among controls. Sex distribution did not differ (MDE, 13 [52%] male; mixed/mania syndromes, 8 [62%] male; controls, 25 [66%] male; P = .56). Kicer (controlling for age and sex) was significant across groups in whole striatum (F2,71 = 4.04; P = .02; R2 = 0.13). People with psychosis and MDE had lower Kicer compared with those with psychosis and mixed/mania syndromes (β [SE], 0.014 [0.001]; P = .02), with the largest difference observed in the limbic striatum (Cohen d = 1.57; P
Application of machine learning in early childhood development research: A scoping review
Background Early childhood development (ECD) lays the foundation for lifelong health, academic success and social well-being, yet over 250 million children in low- and middle-income countries are at risk of not reaching their developmental potential. Traditional measures fail to fully capture the risks associated with a child's development outcomes. Artificial intelligence techniques, particularly machine learning (ML), offer an innovative approach by analysing complex datasets to detect subtle developmental patterns. Objective To map the existing literature on the use of ML in ECD research, including its geographical distribution, to identify research gaps and inform future directions. The review focuses on applied ML techniques, data types, feature sets, outcomes, data splitting and validation strategies, model performance, model explainability, key themes, clinical relevance and reported limitations. Design Scoping review using the Arksey and O € Malley framework with enhancements by Levac et al. Data sources A systematic search was conducted on 16 June 2024 across PubMed, Web of Science, IEEE Xplore and PsycINFO, supplemented by grey literature (OpenGrey) and reference hand-searching. No publication date limits were applied. Eligibility criteria Included studies applied ML or its variants (eg, deep learning (DL), natural language processing) to developmental outcomes in children aged 0-8 years. Studies were in English and addressed cognitive, language, motor or social-emotional development. Excluded were studies focusing on robotics; neurodevelopmental disorders such as autism spectrum disorder, attention-deficit/hyperactivity disorder and communication disorders; disease or medical conditions; and review articles. Data extraction and charting Three reviewers independently extracted data using a structured MS Excel template, covering study ML techniques, data types, feature sets, outcomes, outcome measures, data splitting and validation strategies, model performance, model explainability, key themes, clinical relevance and limitations. A narrative synthesis was conducted, supported by descriptive statistics and visualisations. Results Of the 759 articles retrieved, 27 met the inclusion criteria. Most studies (78%) originated from high-income countries, with none from sub-Saharan Africa. Supervised ML classifiers (40.7%) and DL techniques (22.2%) were the most used approaches. Cognitive development was the most frequently targeted outcome (33.3%), often measured using the Bayley Scales of Infant and Toddler Development-III (33.3%). Data types varied, with image, video and sensor-based data being most prevalent. Key predictive features were grouped into six categories: brain features; anthropometric and clinical/biological markers; socio-demographic and environmental factors; medical history and nutritional indicators; linguistic and expressive features; and motor indicators. Most studies (74.1%) focused solely on prediction, with the majority conducting predictions at age 2 years and above. Only 41% of studies employed explainability methods, and validation strategies varied widely. Few studies (7.4%) conducted external validation, and only one had progressed to a clinical trial. Common limitations included small sample sizes, lack of external validation and imbalanced datasets. Conclusion There is growing interest in using ML for ECD research, but current research lacks geographical diversity, external validation, explainability and practical implementation. Future work should focus on developing inclusive, interpretable and externally validated models that are integrated into real-world implementation.
Blurring Boundaries: The Role of Hybrid Green Spaces in Secure Psychiatric Care
Hybrid Green Spaces in psychiatric intensive care units offer a transformative approach to mental healthcare environments, addressing tensions between therapeutic intent and institutional control. Drawing on our CAMHS PICU case, we demonstrate how (co)produced biodiverse outdoor spaces can actively mediate challenges across risk management, spatial production, and power dynamics. These spaces foster relationships between human and ecological wellbeing, promoting what we call Ecological Collective Flourishing. By enhancing staff wellbeing, creating moments of shared stewardship, and expanding therapeutic possibilities such interventions show that even highly controlled clinical settings can accommodate nature-based programmes safely and meaningfully. We argue that these hybrid spaces hold significant potential for broader application across psychiatric services, supporting patient-centred care goals, institutional resilience, and environmental sustainability. Our case challenges assumptions about what is possible in secure mental health settings, offering a replicable model for integrating nature-based approaches into psychiatric care without compromising safety protocols.
Psychosis in Huntington's disease: a review and comparison with schizophrenia.
Psychosis is a relatively rare phenomenon in Huntington's disease (HD) yet it occurs more commonly amongst individuals with HD than in the general population. Its presence is associated with significant distress and caregiver burden. This review evaluates the epidemiology, aetiology, phenomenology, neurobiology and treatment of psychosis in HD, drawing comparisons with schizophrenia as an archetypal psychotic disorder. We conducted a detailed literature search and narrative synthesis and found that prevalence estimates of psychosis in HD varied widely (4.1-17.6 %). While generally more common in those with established motor symptoms, psychosis occurred throughout the HD course. Its presence conferred a poorer prognosis, including greater functional and cognitive decline. No distinct phenomenology of psychosis in HD emerged; paranoid ideation was common whereas formal thought disorder was rarely reported. Like schizophrenia, psychosis in HD is associated with depression, suicidality, apathy, executive and social cognitive dysfunction. The neurobiology of psychosis in HD is not well understood however HD neurobiology shares some overlap with schizophrenia. Despite the absence of mesostriatal hyperdopaminergic transmission, frontostriatal network dysfunction, glutamatergic dysregulation and medium spiny neuron pathology could contribute to psychosis manifestation. The development of psychosis in HD is conceptualised within a stress-diathesis framework, involving an interaction between genetic risk (with some shared vulnerability to schizophrenia), neuronal changes and psychosocial stressors. Clinically, this implies a rationale for utilising therapeutic approaches trialled in schizophrenia, as there is no evidence that psychosis in HD requires fundamentally different treatment, except for an awareness of the antipsychotic effects on HD motor symptoms.
Why is Clozapine uniquely Effective in Treatment Resistant Schizophrenia?
Much is known about the use, benefits, and side-effects, of clozapine in treatment resistant schizophrenia (TRS). However, why clozapine is more effective than other antipsychotics in TRS remains unclear. This paper addresses this question. TRS patients show glutamate abnormalities, and clozapine has widespread effects on glutamate. However, these actions have not been proven different to those of other antipsychotics. Immune dysfunction is also reported in TRS, and clozapine has anti-inflammatory actions, but these have not been correlated with clinical improvement. Currently, there is much interest in muscarinic abnormalities in psychosis. Unlike most antipsychotics, clozapine has important effects on muscarinic receptors, particularly M1 and M4, and its major metabolite, N-desmethylclozapine, is a full agonist at M1. These effects are likely crucial to clozapine's effectiveness. In addition, clozapine's lower D2 receptor occupancy has been postulated to allow gradual resolution of dopamine receptor supersensitivity in the minority of patients with TRS who initially respond to antipsychotics but become resistant following long-term dopamine blockade. This hypothesis, however, remains controversial. Clozapine's multi-receptor profile enables it to have beneficial actions on the non-psychotic symptoms common in TRS: its ability to bind to histamine H1, serotonin 5-HT1A and GABA-B receptors offers an explanation for its anxiolytic actions while effects on 5-HT1A, 5-HT2A and 5-HT7 receptors likely underly its antidepressant properties. Clozapine shares these properties with olanzapine and quetiapine but its affinity for muscarinic receptors may be the mechanism by which it is more effective in TRS.
Contextualization and Adaptation of the Child and Adolescent Mental and Behavioural Disorders Module of the mhGAP-IG in Kilifi and Nairobi Counties in Kenya
The Mental Health Gap Action Programme Intervention Guide (mhGAP-IG) was developed by the World Health Organization as a key technical tool for delivering evidence-based mental healthcare in non-specialized settings around the world. It requires contextualization and adaptation for local relevance, considering healthcare and resource contexts. However, evidence on adapting the Child and Adolescent Mental Disorders module of the mhGAP-IG is limited. This study involved contextualizing and adapting the Child and Adolescent Mental Disorders module of the 2016 mhGAP-IG through two workshops with local mental health experts and stakeholders. Prior to the workshops, six in-depth interviews were conducted with mental health stakeholders to explore the child and adolescent mental health system contexts in Nairobi and Kilifi. Data were analysed using thematic analysis in NVivo-Lumivero© software. Interviews with mental health stakeholders revealed significant challenges in both counties, including a shortage of mental health specialists, frequent medication stockouts, stigma, and inadequate resources. Key adaptations to the module included using locally acceptable terms such as changing ‘failure to thrive’ to ‘sub-optimal growth’; expanding the training to five days; inclusion of the mhGAP-IG Essential Care and Practice module to address culturally sensitive communication in care provision; streamlining referral pathways; and incorporating aspects of self-harm, suicide, and substance use linked to the child and adolescent mental and behavioural disorders module. Contextualizing the child and adolescent mental disorders module is crucial for effective implementation. However, sustaining its impact will require addressing systemic barriers beyond the capacity-building efforts.
Regional and Global Implications for Children’s Brain Health
The majority of the world’s children with neurological disorders live in low- and middle-income countries (LMICs) but still lack access to specialty care. There remains a bias in resource allocation to high-income settings; this includes the focus of research that historically has not accommodated children in LMICs. As such, recommendations are driven from high-income setting data. Compounding influences on brain health in LMICs include poverty, malnutrition, environmental toxins, impact of war and displacement, and failure of prevention programs (e.g., vaccination roll-out). Further, in these settings, the neurologic burden of infections and neuroinfectious is high. Globally, other detrimental influences on brain health are obesity and excessive screen time, which are becoming prevalent regardless of location.
Pain and suicidality during adolescence: a Danish National Cohort Study
Abstract Pain has been suggested as an important risk factor for suicidality in adolescents. We examined the association between pain at age 11 years and suicidality until age 18 years. Second, we assessed whether psychiatric diagnoses might mediate this association. We used data from the Danish National Birth Cohort's 11-year follow-up (DNBC-11) and 18-year follow-up (DNBC-18). Self-reported head, stomach, neck, and back pain at age 11 years were examined as exposures. Outcomes were formed from data on self-reported suicidal ideation and suicide attempts from the DNBC-18 and hospital-recorded suicide attempts by age 18 years. We used multinomial logistic regressions and mediation analyses, adjusting for covariates and incorporating sampling weights. Among 28,465 eleven-year-olds, 13.5% reported any frequent pain, which was associated with increased risks of suicidal ideation (adjusted relative risk ratio [aRRR] = 1.6, 95% confidence interval [CI]: 1.5-1.7) and suicide attempts (aRRR = 2.4, 95% CI: 2.0-2.8). Individuals who had reported 3 or more pain sites at age 11 years had a higher risk of suicide attempt (aRRR = 6.4, 95% CI: 3.9-10.4) compared with those with no frequent pain. Pain-related functional interference and recurrent pain were associated with significantly elevated risks of suicidal ideation and suicide attempts. Affective and anxiety/stress-related disorders diagnosed between age 11 and 18 years significantly mediated the association between frequent pain and suicidal ideation (14%-16%), as well as between frequent pain and suicide attempts (37%-48%). Frequent pain is a common concern in 11-year-olds in Denmark and prospectively associated with an increased risk of suicidality by age 18 years. Suicide preventive strategies may consider targeting youth with frequent pain.