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Today, 10th October is the Thames Valley Suicide Prevention Conference in Reading from the Thames Valley Suicide Prevention and Intervention Network (SPIN). Professor Keith Hawton, Director of the Centre for Suicide Research, Department of Psychiatry, University of Oxford, will be chairing the event.
Are Adolescents Sensitive About Sensitive Data? Exploring Student Concerns About Privacy, Confidentiality, and Data Use in Health Research.
PURPOSE: We quantitively explored adolescents' concerns about privacy, confidentiality, and data use in health research and their potential impact on the accuracy of self-report data. METHODS: We analyzed data from 17,729 secondary school students who participated in the 2023 OxWell Student Survey. The survey assessed 5 concerns about privacy, confidentiality, and data use and asked students whether these concerns impacted the accuracy of their answers. We calculated the proportions who (a) endorsed each concern and (b) reported inaccuracies associated with their concern(s). We then examined associations of concerns and self-reported inaccuracies with nonresponse and score distributions on sensitive measures of mental illness (depression/anxiety and disordered eating) and adversity (child maltreatment) using logistic regression. RESULTS: 46.0% (8,160/17,729) of students endorsed ≥1 concern, and of these, 29.2% (2,379/8,160) reported associated inaccuracies. Relative to boys, concerns were more common amongst gender diverse adolescents (adjusted odds ratio [aOR] = 5.71, 95% confidence interval [CI] 4.40-7.48), gender nondisclosing adolescents (aOR = 4.36, 95% CI 3.62-5.26), and girls (aOR = 2.52, 95% CI 2.36-2.69), with smaller differences in self-reported inaccuracies. Students with self-reported inaccuracies were significantly more likely to have nonresponse on the 3 measures of mental illness and adversity (aORs = 1.53-3.38), whilst score distributions on those measures varied substantially according to whether students reported concerns. DISCUSSION: Concerns about privacy, confidentiality, and data use were common amongst student participants, as were self-reported inaccuracies. Substantial differences in nonresponse and score distributions on sensitive measures highlight potential impacts of these concerns. Co-designing and implementing strategies to address these concerns might help to support evidence-based decision-making by improving representativeness and data quality in adolescent health research.
Healthcare professionals' perspectives on assessing selected patient-reported outcome measures in specialist palliative care institutions: a multi-country mixed-methods study.
BACKGROUND: Despite the growing significance of patient-reported outcome measures (PROMs) for various purposes, including economic evaluations, implementing them effectively in palliative and end-of-life care settings remains a challenge. This study aimed to identify barriers and facilitators to PROMs data collection in inpatient specialist palliative care settings and to assess data collectors' applied perspectives on four relevant PROMs. METHODS: We conducted an explanatory sequential mixed-methods study, including an online survey (N = 29) and qualitative interviews (N = 12) with healthcare professionals and researchers from eleven countries. These participants had direct experience with PROMs data collection in specialist palliative care settings, either as part of the international iLIVE project or the Austrian PallPROMS study. The aim was to identify opportunities for optimising clinical care and other assessment purposes in the future. We conducted a descriptive analysis of the survey data and a thematic analysis of the qualitative data. RESULTS: The main reflected factors were patients' very limited ability to self-complete PROMs and the optimal timing and duration of assessments. Opinions on the usefulness of different PROMs varied significantly according to the role of the participants. Overall, setting-specific PROMs assessing symptom burden were preferred to more generic quality-of-life/wellbeing measures. Identified barriers and facilitators related to five themes: patient-related factors, data collection processes, PROM type, staff perceptions and organisational factors. Findings also highlighted better information and training needs. CONCLUSIONS: Prioritising care-relevant tools and carefully planning data collection, with main barriers addressed, can significantly increase the successful implementation of PROMs collection in specialist palliative care institutions. Since the preferred PROMs are not directly suitable for health economic evaluation, it is crucial to explore mapping alternatives for this purpose.
Brief use of behavioral activation features predicts benefits of self-help app on depression symptoms: Secondary analysis of a selective prevention trial in young people.
OBJECTIVE: To explore which cognitive behavioral therapy (CBT) self-help app usage predicted depression during a selective prevention trial. METHOD: A recent controlled trial (ECoWeB-PREVENT) randomized young people aged 16-22, at increased risk for depression because of elevated worry/rumination, negative appraisals, and/or rejection sensitivity but without past or current history of major depression, to apps that provided self-monitoring, self-monitoring plus CBT self-help, or self-monitoring plus emotional competency self-help. Self-help included coping strategies for moment-by-moment use (Tools) and self-learning/planning exercises (Challenges). On the primary outcome (depression, Patient Health Questionnaire-9 [PHQ-9]) at 3-months follow-up (primary endpoint), only the CBT app outperformed self-monitoring. In this secondary analysis, only data from participants who used the CBT or self-monitoring apps at least once were analyzed to test what app use predicted change in depression from baseline to 3 months. RESULTS: Of the original 1,262 participants (79% female), 558 were included (CBT, baseline, n = 273, PHQ-9: M = 7.48, SD = 3.9; 3 months, N = 163, PHQ-9: M = 8.83, SD = 4.92; self-monitoring, baseline, n = 285, PHQ-9: M = 7.45, SD = 4.26; 3 months, N = 183, PHQ-9: M = 7.48, SD = 3.9). Neither total app use, self-monitoring, nor use of Tools predicted change in depression (all ps > .05). Frequency of use of Challenges predicted lower depression symptoms and caseness at 3 months (β = -0.28, 95% CI [-0.53, -0.03], p = .029). Specifically, the use of behavioral activation challenges mediated the effects of the CBT app on depression over 3 months (β = -0.59, 95% CI [-1.13, -0.05], p = .03). CONCLUSIONS: Brief psychoeducation about behavioral activation principles in an app may protect young people from depression over 3 months, even when only used once. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
Machine learning-based prediction of anxiety disorders using blood metabolite and social trait data from the UK Biobank
Anxiety disorders are the most prevalent type of mental health disorders and are characterised by excessive fear and worry. Despite affecting one in four individuals within their lifetime, there remains a gap in our understanding regarding the underlying pathophysiology of anxiety disorders, which limits the development of novel treatment options. Exploring blood-based biomarkers of anxiety disorder offers the potential to predict the risk of clinically significant anxiety in the general population, increase our understanding of anxiety pathophysiology, and to reveal options for preventative treatment. Here, using psychosocial variables in combination with blood and urine biomarkers, reported in the UK Biobank, we sought to predict future anxiety onset. Machine learning accurately predicted (ROC AUC: ∼0.83) ICD-10-coded anxiety diagnoses up to 5 years (mean 3.5 years) after blood sampling, against lifetime anxiety-free controls. Analysis of the blood biochemistry measures indicated that anxious individuals were more anaemic and exhibited higher levels of markers of systemic inflammation than controls. However, blood biomarkers alone were not predictive of resilience or susceptibility to anxiety disorders in a subset of individuals rigorously matched for a wide range of psychosocial covariates (ROC AUC: ∼0.50). Overall, we demonstrate that the integration of biological and psychosocial risk factors is an effective tool to screen for and predict anxiety disorder onset in the general population.
What Causes the Onset of Psychosis in Individuals at Clinical High Risk? A Meta-analysis of Risk and Protective Factors.
Twenty percent of individuals at clinical high risk for psychosis (CHR-P) develop the disorder within 2 years. Extensive research has explored the factors that differentiate those who develop psychosis and those who do not, but the results are conflicting. The current systematic review and meta-analysis comprehensively addresses the consistency and magnitude of evidence for non-purely genetic risk and protective factors associated with the risk of developing psychosis in CHR-P individuals. Random effects meta-analyses, standardized mean difference (SMD) and odds ratio (OR) were used, in combination with an established stratification of evidence that assesses the association of each factor and the onset of psychotic disorders (from class I, convincing evidence to class IV weak evidence), while controlling for several types of biases. A total of 128 original controlled studies relating to 26 factors were retrieved. No factors showed class I-convincing evidence. Two further factors were associated with class II-highly suggestive evidence: attenuated positive psychotic symptoms (SMD = 0.348, 95% CI: 0.280, 0.415) and global functioning (SMD = -0.291, 95% CI: -0.370, -0.211). There was class III-suggestive evidence for negative psychotic symptoms (SMD = 0.393, 95% CI: 0.317, 0.469). There was either class IV-weak or no evidence for all other factors. Our findings suggest that despite the large number of putative risk factors investigated in the literature, only attenuated positive psychotic symptoms, global functioning, and negative psychotic symptoms show suggestive evidence or greater for association with transition to psychosis. The current findings may inform the refinement of clinical prediction models and precision medicine in this field.
Women's reproductive mental health: currently available evidence and future directions for research, clinical practice and health policy
Sex and gender differences in the epidemiology of mental disorders are well documented. Less well understood are the drivers of these differences. Reproductive health represents one of the gendered determinants of mental health that may affect women throughout their life course. In this paper, we review common reproductive events that may be associated with mental ill health, including menstruation (with premenstrual dysphoric disorder appearing for the first time in recent classifications of mental disorders), contraception, abortion, sexual dysfunction, hypersexuality, sexual violence, reproductive coercion, infertility and associated gynaecological conditions, and menopause. Such reproductive events may differentially affect women globally via a range of potential biological and psychosocial mechanisms. These include, for example, vulnerability to the physiological changes in hormone levels across the menstrual cycle; side effects of treatment of mental disorders; inflammation underpinning endometriosis and polycystic ovarian syndrome as well as mental disorders such as depression; intersections with gender disadvantage manifesting, for example, as structural barriers in accessing menstrual products and sanitation, contraception and abortion, underscoring the broader social determinants impacting women's mental health. Greater understanding of these mechanisms is guiding the development of effective interventions, which are also reviewed here. However, key evidence gaps remain, partly as a result of the historic gender bias in mental health research, and the neglect of reproductive health in clinical practice. Furthermore, while several women's health strategies have recently been proposed internationally, they do not usually include a focus on mental health across the life course, particularly for women with severe mental illness. Integrating co‐designed reproductive health interventions into primary and secondary mental health care settings, providing tailored care, increasing the evidence base on effective interventions, and empowering women to make informed choices about their reproductive health, could improve not only reproductive health but also women's mental health across the life course.
Serum NOX1 and Raftlin as New Potential Biomarkers of Interest in Schizophrenia: A Preliminary Study
Introduction: There is increasing evidence that oxidative stress (OS) and neuroinflammation play a role in the neuroprogression of schizophrenia (SCZ). Promising novel candidates which have been proposed in the search for biomarkers of psychotic illness include NADPH oxidase 1,2 (NOX1,2) and raftlin. NOX1 from the NOX family is the main source of physiological reactive oxygen species (ROS) and raftlin, the main lipid raft protein, is associated with inflammatory processes. The aim of the present study was to evaluate serum NOX1 and raftlin levels in chronic stable patients with SCZ. Methods: We measured serum NOX1 and raftlin levels from 45 clinically stable patients with SCZ and 45 healthy controls (HCs) matched for age, sex, and body-mass index. The Positive and Negative Syndrome Scale was applied to the patient group to evaluate the severity of psychotic symptoms. Results: NOX1 and raftlin levels in the patients were statistically significantly higher than the HCs (NOX1 p<0.001, raftlin p<0.001). Both parameters showed very good diagnostic performance (NOX1 AUC = 0.931, raftlin AUC = 0.915). We obtained positive and significant correlations between serum levels of both biomarkers and symptom severity. Discussion: This preliminary study indicating elevations in serum NOX1 and raftlin levels in patients with SCZ supports the importance of OS and inflammatory processes in the etiopathogenesis of the illness.
Gamma band oscillations in the early phase of psychosis: A systematic review
Abnormal gamma oscillations, measured by electroencephalography (EEG), have been associated with chronic psychotic disorders, but their prevalence in the early phase of psychosis is less clear. We sought to address this by systematically reviewing the relevant literature. We searched for EEG studies of gamma band oscillations in subjects at high risk for psychosis and in patients with first episode psychosis. The following measures of gamma oscillations were extracted: resting power, evoked power, induced power, connectivity and peak frequency. Forty-five studies with a total of 3099 participants were included. There were potential sources of bias in the study designs and potential artefacts. Although there were few consistent findings, several studies reported decreased evoked or induced power in both high risk subjects and first episode patients. Studies using larger samples with serial EEG measurements, and designs that minimise artefacts that occur at the gamma frequency may advance work in this area.
Improving Prognostic Accuracy in Subjects at Clinical High Risk for Psychosis: Systematic Review of Predictive Models and Meta-analytical Sequential Testing Simulation
Discriminating subjects at clinical high risk (CHR) for psychosis who will develop psychosis from those who will not is a prerequisite for preventive treatments. However, it is not yet possible to make any personalized prediction of psychosis onset relying only on the initial clinical baseline assessment. Here, we first present a systematic review of prognostic accuracy parameters of predictive modeling studies using clinical, biological, neurocognitive, environmental, and combinations of predictors. In a second step, we performed statistical simulations to test different probabilistic sequential 3-stage testing strategies aimed at improving prognostic accuracy on top of the clinical baseline assessment. The systematic review revealed that the best environmental predictive model yielded a modest positive predictive value (PPV) (63%). Conversely, the best predictive models in other domains (clinical, biological, neurocognitive, and combined models) yielded PPVs of above 82%. Using only data from validated models, 3-stage simulations showed that the highest PPV was achieved by sequentially using a combined (clinical + electroencephalography), then structural magnetic resonance imaging and then a blood markers model. Specifically, PPV was estimated to be 98% (number needed to treat, NNT = 2) for an individual with 3 positive sequential tests, 71%-82% (NNT = 3) with 2 positive tests, 12%-21% (NNT = 11-18) with 1 positive test, and 1% (NNT = 219) for an individual with no positive tests. This work suggests that sequentially testing CHR subjects with predictive models across multiple domains may substantially improve psychosis prediction following the initial CHR assessment. Multistage sequential testing may allow individual risk stratification of CHR individuals and optimize the prediction of psychosis.
The neurology-psychiatry divide: A thought experiment
Diseases of the brain are generally classified as either neurological or psychiatric. However, these two groups of illnesses cannot be readily separated on the basis of pathophysiology or symptomatology. It is difficult to rationally explain to someone with no prior frame of reference why we have the split between neurological and psychiatric illness. This demonstrates that the division is untenable, which has implications for training in both psychiatry and neurology.
Translating neuroimaging findings into psychiatric practice
Although translational medicine has become a priority for medical science, advances in neuroscience have failed to be translated for the benefit of patients. In populations at high risk of psychosis, neuroimaging could stratify those mostly likely to develop psychosis. This is an example of potentially translatable psychiatry.
The myth of schizophrenia as a progressive brain disease
Schizophrenia has historically been considered to be a deteriorating disease, a view reinforced by recent MRI findings of progressive brain tissue loss over the early years of illness. On the other hand, the notion that recovery from schizophrenia is possible is increasingly embraced by consumer and family groups. This review critically examines the evidence from longitudinal studies of (1) clinical outcomes, (2) MRI brain volumes, and (3) cognitive functioning. First, the evidence shows that although approximately 25% of people with schizophrenia have a poor long-term outcome, few of these show the incremental loss of function that is characteristic of neurodegenerative illnesses. Second, MRI studies demonstrate subtle developmental abnormalities at first onset of psychosis and then further decreases in brain tissue volumes; however, these latter decreases are explicable by the effects of antipsychotic medication, substance abuse, and other secondary factors. Third, while patients do show cognitive deficits compared with controls, cognitive functioning does not appear to deteriorate over time. The majority of people with schizophrenia have the potential to achieve long-term remission and functional recovery. The fact that some experience deterioration in functioning over time may reflect poor access, or adherence, to treatment, the effects of concurrent conditions, and social and financial impoverishment. Mental health professionals need to join with patients and their families in understanding that schizophrenia is not a malignant disease that inevitably deteriorates over time but rather one from which most people can achieve a substantial degree of recovery. © 2012 The Author.
Are cognitive behavioural therapy, cognitive therapy, and behavioural activation for depression effective in primary care? A systematic review and meta-analysis.
Cognitive behavioural therapy (CBT) is a recommended first-line treatment for depression. Evidence mainly derives from studies in secondary care, though most treatment occurs in primary care. This review examined efficacy of CBT, cognitive therapy (CT), or behavioural activation (BA) for depression within primary care. Databases were searched for trials up to 23rd July 2024. Risk of bias was assessed using the Cochrane risk-of-bias tool, version 2.0.44 studies were included. CBT, CT, and BA significantly reduced depression symptoms compared to inactive controls (k = 40, g = 0.44, p
Smartphone-based Monitoring and cognition Modification Against Recurrence of Depression (SMARD): An RCT of Memory Bias Modification Training vs. Cognitive Control Training vs. Attention Bias Modification Training in remitted recurrently depressed patients with 1.5 year follow-up.
BACKGROUND: Major Depressive Disorder (MDD) has a 50-80% recurrence rate highlighting the urgent need for more efficient recurrence prevention programs. Currently, recurrences are often identified too late, while existing preventive strategies may not sufficiently address ethio-patho-physiological mechanisms for recurrence. Negative memory bias (the tendency to better remember negative than positive events), negative attention bias (selective attention favoring mood-congruent information), and cognitive control deficits are important factors involved in the onset, maintenance, and recurrence of depressive episodes. METHODS: Here we describe the protocol for the Smartphone-based Monitoring and cognition Modification Against Recurrence of Depression (SMARD) study, aiming to investigate different forms of cognitive training programs administered via smartphones, in order to develop a second-generation recurrence prevention program. In addition, we will gather Experience Sampling Method (ESM) assessments during a 6-day period, and during the follow-up period we will obtain behavioral data on (social) activities with BEHAPP, a smartphone-based Mobile Passive Monitoring application for remote behavioral monitoring to identify behavioral changes indicative of an imminent depressive episode. In a randomized controlled trial, SMARD will compare the effects of a smartphone-based Memory Bias Modification Training (MBT), Cognitive Control Training (CCT), and Attention Bias Modification Training (ABT) versus cognitive domain-specific (active-) sham trainings in 120 remitted MDD-patients with recurrent-MDD. Over the course of three weeks, participants receive multiple daily training sessions. Thereafter, participants will be followed up for 1.5 years with 3-monthly interviews to assess recurrences. DISCUSSION: The SMARD study aims to 1. assess the effects of the cognitive training programs versus their training-specific (active-) sham conditions on changes in memory, cognitive control dysfunction and attention; 2. relate training effects to neural networks previously identified in (recurrence of) MDD (therefore we obtain functional Magnetic Resonance Imaging ((f)MRI) scans before and after the training in a subset of participants); 3. link baseline and change in memory, cognitive control, attention and neural functioning, and ESM data to prospective recurrences; 4. examine whether passive smartphone-use monitoring can be used for prediction of recurrences. TRIAL REGISTRATION: NL-OMON26184 and NL-OMON27513. Registered 12 August 2021-Retrospectively registered, https://onderzoekmetmensen.nl/en/trial/26184 en https://onderzoekmetmensen.nl/en/trial/27513 .
Young people's attitudes towards online self-help single-session interventions: findings from a co-produced qualitative study.
BACKGROUND: Many young people experience at least subthreshold depression symptoms which impact their functioning. Yet, access to evidence-based help is limited, with barriers such as service thresholds, stigma, and lack of knowledge about mental health and available services. One way to ensure young people have access to free, early, immediate and anonymous help is through online self-help single-session interventions. This study aimed to qualitatively explore young people's perceptions of and attitudes towards these interventions. METHODS: Twenty-four young people (UK based, age 15-18) took part in qualitative semi-structured interviews which were hosted online and co-conducted with a young research team (N = 4, age 16-18), during which we described online single-session interventions and asked participants for their perspectives. Together with our young researchers, we analyzed the data using reflexive thematic analysis. RESULTS: Three themes were generated: (1) Will it help, or won't it? Hope versus skepticism; (2) Why this approach? Benefits of single-session interventions for young people; and (3) Have you considered this? Logistics for implementation. CONCLUSIONS: The current study highlights that whilst young people perceived there to be many benefits associated with online single-session interventions, including anonymity, easy access, and lack of disclosure, they expressed doubts regarding sufficiency and ability to address severe mental health problems. Despite this, the potentially preventative effects during the early stages of help-seeking were highlighted, alongside single-session interventions acting as a gateway to further help-seeking and support. However, logistical considerations should also be reflected upon when developing online single-session interventions, including where they are advertised, age appropriateness, and how to demonstrate trustworthiness.
Mapping the Dynamics of Generalized Anxiety Symptoms and Actionable Transdiagnostic Mechanisms: A Panel Study
Background: The long‐term dynamic interaction between symptoms of generalized anxiety disorder (GAD) and their theorized mechanistic processes derived from three treatment models of GAD—the emotion dysregulation model, the model underlying metacognitive therapy (MCT), and the intolerance of uncertainty model—was investigated.Methods: Four data waves 2 months apart were delivered by a representative population sample of 4361 participants during the COVID‐19 pandemic in Norway. Networks were estimated using the newly developed panel graphical vector autoregression (panel‐GVAR) methods.Results: In the temporal network, and consistent with processes stipulated in the metacognitive model, the experience that worry is uncontrollable predicted the GAD symptom fear of awful events, which in turn predicted a range of other GAD symptoms, that is, anxiety, restlessness, and irritability. Fear of awful events had high outstrength, that is, predicted other variables to a large degree. Inconsistent with the metacognitive model, the coping strategy thought suppression negatively predicted restlessness. Consistent with the emotion dysregulation model, emotion dysregulation predicted avoidance. No relationships proposed by the intolerance of uncertainty model of GAD were identified in the temporal network. The contemporaneous network was dense with nodes clustering according to the constructs they belonged to.Conclusions: The findings indicate the importance of the theory‐derived variables, the experience and belief that worry is uncontrollable and emotion dysregulation, as potential targets for intervention to alleviate GAD symptoms. The findings also indicate that uncontrollability of worry and fear of awful events should be considered central symptoms of GAD in a within‐individual diagnostics supplementary to current diagnostic manuals, such as the Diagnostic and Statistical Manual of Mental Disorders 5th edition (DSM‐5).