Tag: United Kingdom

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Effects of Social Media Narratives on Affective and Behavioral Responses to Menopause Content: Randomized Online Experimental Study

Background: Social media is an increasingly prominent channel for communicating menopause information and experiences, yet the affective and behavioral consequences of different narrative framings remain unclear. Objective: We examined how distress, normalizing, and transformative narratives influenced women’s immediate responses to menopause content online, drawing on established narrative framings of menopause as normality, distress, and transformation. Methods: In an online experiment, UK women aged 40 to 83 years who were perimenopausal or postmenopausal were recruited via Prolific, a widely used online recruitment platform for behavioral and social science research. A total of 737 women were randomly assigned to view 4 anonymized and standardized social media posts from a pool of 12 reflecting 1 of 3 narratives: <i>normal</i> (n=248, 33.6%), <i>distress</i> (n=241, 32.7%), or <i>transformative</i> (n=248, 33.6%). Participants then reported affective reactions, expected behavioral responses, and perceptions of the posts using 5-point ordered response scales. Ordinal logistic regression models tested demographic predictors and condition effects controlling for demographic factors. Results: Participants who viewed distress-framed posts reported greater levels of worry (β=.910; <i>P</i><.001), confusion (β=.818; <i>P</i><.001), and anxiety (β=.817; <i>P</i><.001) and lower levels of reassurance (β=−.970; <i>P</i><.001), optimism (β=−.708; <i>P</i><.001), and empowerment (β=−.540; <i>P</i><.001). Distress framing also increased perceived knowledge of menopause (β=.564; <i>P</i><.001) despite participants feeling more negatively toward the posts. Neither distress nor transformative narratives influenced expected behavioral intentions to like, share, save, comment on, search for, or discuss social media posts compared with normalizing narratives. Postmenopausal status (β=−.630; <i>P</i><.001) and older age (β=−.492; <i>P</i><.001) were independently associated with less worry and anxiety. Participants rated distress (β=−.806; <i>P</i><.001) and transformative posts (β=−.968; <i>P</i><.001) as less representative of health professionals than normalizing posts; transformative posts were also judged to be less representative of newspapers or television (β=−.687; <i>P</i><.001). Conclusions: Narrative framing shaped immediate affect but not intended engagement with menopause content. Because this study assessed short-term responses to controlled, standardized posts, future research should examine whether these effects persist over time and how they operate in more ecologically valid social media environments. As public discussion expands, diverse, balanced narratives may help reduce stigma and temper the disproportionate salience of negative framing. This study advances understanding of how narrative framing shapes responses to health content online.
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Pharmacotherapy, acupoint stimulation, and psychotherapy for perimenopausal women with anxiety, depression, and panic disorder: a systematic review and network meta-analysis of randomized controlled trials

BackgroundPerimenopausal women frequently experience physiological and psychological symptoms, including anxiety, depression, and panic disorders, mainly due to declining ovarian function and hormonal changes. Current options include pharmacotherapy, acupoint stimulation (AcuStim), and psychotherapy (psych), but their comparative efficacy and safety remain controversial.ObjectiveThis network meta-analysis (NMA) systematically compared pharmacotherapy, AcuStim, and psychotherapy for perimenopausal anxiety, depression, and panic disorder, assessing clinical efficacy, adverse events (AEs), and changes in the Hamilton Depression Rating Scale (HAMD), Hamilton Anxiety Rating Scale (HAMA), Kupperman Index (KI), Self-rating Depression Scale (SDS), Self-rating Anxiety Scale (SAS), Pittsburgh Sleep Quality Index (PSQI), and serum hormone levels.MethodsWe searched PubMed, Embase, Cochrane Library, Web of Science, CNKI, Wanfang, VIP, and SinoMed from inception to June 14, 2026, for randomized controlled trials (RCTs). A Bayesian NMA was performed, and the Surface Under the Cumulative Ranking Curve (SUCRA) was calculated.ResultsThe study included 131 RCTs, encompassing 11457 perimenopausal women diagnosed with emotional disorders. These trials evaluated three distinct treatment strategies. The NMA showed that the highest SUCRA probabilities were observed for drug_psych across HAMD (SUCRA = 92.4%), KI (SUCRA = 97.9%), SDS (SUCRA = 94.5%), PSQI (SUCRA = 98.1%), and follicle-stimulating hormone (FSH) (SUCRA = 96.1%) reduction and estradiol (E2) (SUCRA = 0.1%) elevation; for AcuStim_psych (SUCRA = 93.7%) in HAMA reduction; for psych (SUCRA = 98.9%) in SAS reduction; for drug_AcuStim in clinical efficacy (SUCRA = 9.0%) and luteinizing hormone (LH) reduction (SUCRA = 100%); and for control (SUCRA = 65.5%) in safety outcomes. In pharmacotherapy subgroup analyses, antidepressants (ADs)_Traditional Chinese medicine (TCM) ranked highest for HAMD (SUCRA = 87.2%) and safety (SUCRA = 82%), ADs_antipsychotics (AP) (SUCRA = 97.5%) for HAMA, and ADs_hormone replacement therapy (HRT) (SUCRA = 10.2%) for clinical efficacy.ConclusionPharmacological, acupoint stimulation, and psychological interventions each demonstrated therapeutic benefits for perimenopausal women with emotional disorders. Combination therapies generally showed more favorable efficacy across multiple psychological and endocrine outcomes than single-modality interventions, while no single treatment strategy was consistently superior across all outcomes. These findings may provide evidence to support individualized treatment selection according to patients’ clinical characteristics and therapeutic goals.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD420261340530.
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Blood Test Foresees Decline into Alzheimer’s Disease

A blood-based biomarker could predict a person’s risk of developing Alzheimer’s disease years before any symptoms arise, research suggests.

Plasma levels of phosphorylated tau 217 (p‑tau217) may one day help identify at-risk individuals before overt signs of dementia, enabling the pre-emptive use of disease-modifying therapies.

Higher plasma p‑tau217 levels were associated with a greater risk of progressing to cognitive impairment in previously unaffected older adults, and they also predicted faster levels of decline.

The research findings appear in JAMA and were simultaneously presented this week at the annual Alzheimer’s Association International Conference in London.

“In this longitudinal study of several selected cohorts, plasma p-tau217 provided long-term prognostic information for individuals who were cognitively unimpaired at baseline, laying the groundwork for possible future development of individualized risk prediction scores,” proposed Rachel Buckley, PhD, from Mass General Brigham, and co-workers in their published work.

“By providing absolute risk estimates of progression to cognitive impairment, this article moves the field closer to presymptomatic risk stratification with p-tau217, supporting trial design.”

The large, pooled multicohort study included 2684 cognitively unimpaired older adults from six longitudinal studies, who were followed for a median of 5.4 years. Their median age was just short of 70 years, and 63% were women.

Results showed that higher baseline p-tau217 was significantly associated with an increased risk of progression to cognitive impairment during up to 13.5 years of follow up, with a hazard ratio of 1.38 per standard deviation (SD) increase.

This remained significant after accounting for age, sex, education, apolipoprotein E ε4 status, cohort, as well as amyloid positron emission tomography—known to accurately detect Alzheimer’s disease brain pathology.

The researchers report that the absolute risk of cognitive decline at five years was “meaningfully elevated” in the group with very high p-tau217 levels (≥2.5 SD) at 38%, versus just 12% in group with low levels.

Estimated 10-years risks were substantially higher at between 40% and 78% for the low and high p-tau217 groups, respectively. However, just 139 participants—or one in every 20—were followed up for at least a decade and the researchers say these risk estimates should be treated with caution.

Elevated p-tau217 was also associated with faster decline on the harmonized latent Preclinical Alzheimer Cognitive Composite assessment tool.

In an editorial accompanying the published study, Suzanne Schindler, PhD, from Washington University in St Louis school of medicine, and David Wolk, MD, from the University of Pennsylvania, note that cognitive impairment likely reflected multiple etiologies, not just Alzheimer’s disease.

“Indeed, even the low p-tau217 group, in which Alzheimer disease pathology was minimal or absent, still had a 12% risk of progression to cognitive impairment at five years, suggesting the importance of other drivers of cognitive decline in this population and the likelihood that in the higher p-tau217 groups, some proportion of individuals who declined may have primarily been driven by other processes,” they pointed out.

“Notably, discordance between plasma p-tau217 and amyloid PET (especially at intermediate or low amyloid levels) highlights that biomarkers beyond p-tau217 could further improve risk prediction.”

Nonetheless, overall they summarized: “The study by Buckley et al. represents a significant advance. It demonstrates that plasma p-tau217 can provide a time-specific absolute risk estimate for development of cognitive impairment.”

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Prevalence of pharmacologically treated attention deficit hyperactivity disorder in children, adolescents, and adults: systematic review and meta-analysis

IntroductionAttention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental paediatric disorders and persists into adulthood, although it is frequently underdiagnosed and underrecognized in adult populations. In this context, the prevalence of pharmacologically treated individuals diagnosed with ADHD represents an important quality indicator for ADHD management.AimTo estimate the pooled prevalence of pharmacologically treated individuals with ADHD in different age groups in Europe and worldwide.MethodsA comprehensive search of PubMed/MEDLINE was conducted to identify relevant articles published up to October 4, 2024. The present systematic review and meta-analysis examined ADHD prevalence using clinically confirmed diagnoses and treatment data from official records. The exclusion criteria included studies that lacked clinical confirmation of ADHD and/or relied exclusively on parental reports for diagnostic or medication information. The prevalence of pharmacologically treated individuals with ADHD was calculated as a percentage, with a 95% confidence interval (CI). A meta-analysis was performed in R using a random-effects model. Heterogeneity was calculated using I². Prediction intervals were additionally computed to reflect the expected range of prevalence in future studies. Risk of bias was assessed for all included studies using a standardized, previously published methodology. The study was prospectively registered in PROSPERO (CRD42020200220) and adhered to the PRISMA guidelines for systematic review and meta-analysis (2020).ResultsThe systematic review identified 13 studies (12 studies included in the meta-analysis) with substantial variation in age-specific reporting. The pooled prevalence of pharmacologically treated ADHD was 73.4% (95% CI: 63.4–81.5), with extremely high between-study heterogeneity and wide 95% prediction interval (29.6%–94.5%), reflecting substantial variation across settings. The pooled prevalence estimate should be interpreted with caution due to substantial between-study heterogeneity and is not intended for direct clinical inference. Geographic analyses revealed no significant variation across countries. Sex-stratified analyses showed no significant difference between males and females, although point estimates were slightly higher in males.ConclusionThe prevalence of pharmacological treatment among individuals with ADHD appears to vary across age groups and settings Overall, findings indicate substantial variation in pharmacological treatment of ADHD by age, with consistently high heterogeneity limiting the precision of pooled estimates.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42020200220.
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Efficacy of digital interventions in social anxiety disorder: a systematic review and Bayesian network meta-analysis

BackgroundSocial anxiety disorder (SAD) is characterized by a significant and persistent fear of social or performance situations. The prevalence of SAD has gradually increased recently, and the unique advantages of digital interventions (DIs) have gained traction in psychiatric disorders. However, there is currently no comprehensive review comparing the effectiveness of diverse DIs for SAD.MethodsRandomized controlled trials (RCTs) evaluating DIs for patients with SAD were identified by searching the PubMed, Cochrane Library, and Embase databases from January 1, 1995, to March 31, 2025. The study protocol for this network meta-analysis was registered in PROSPERO. Data were analyzed via Bayesian framework network meta-analysis.ResultsForty-two RCTs were included. The results showed that DIs exerted better efficacy than non-digital interventions and wait-list controls (WLC). Different forms of internet-based cognitive behavioral therapy (ICBT) demonstrated robust effects across all four outcomes. Internet-based cognitive therapy (ICT) yielded favorable effects in reducing social anxiety and depressive symptoms. VR showed relatively large effect sizes for improving quality of life.ConclusionDIs can be recommended as adjunctive or combined treatments for SAD. Different forms of ICBT show consistent efficacy and can serve as the first-line option among digital interventions. We recommend promoting the application of DIs to expand treatment coverage for SAD and overcome the limitations of traditional psychotherapy.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD420251077835.
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Loneliness From the Digital Mental Health Practitioners’ Perspective: Thematic Analysis of Semistructured Interviews

Background: Loneliness is a prevalent concern across the United Kingdom. While validated scales exist to quantify the severity of loneliness across populations, there remains a gap in understanding how loneliness manifests and is addressed within therapeutic practice. Given the associated stigma surrounding loneliness, practitioner perspectives offer crucial insights into how clients express loneliness within digital therapeutic environments. These insights can inform more nuanced conceptualizations of loneliness. Objective: This study aimed to gather the practitioners’ perspectives on loneliness within a digital therapeutic context and were defined as follows: (1) understand how practitioners identify loneliness concerns, (2) identify how loneliness is elicited in digital mental health interventions, and (3) identify co-occurring themes (such as grief, shame, and social disconnection) that signal loneliness concerns in client communications within digital therapeutic environments. Methods: Semistructured interviews were conducted with 9 practitioners. Participants included specialists in grief counseling, lesbian, gay, bisexual, transgender, and queer or questioning plus support; and digital mental health therapists. Interview transcripts were analyzed using thematic analysis, using an inductive, data-driven approach to allow themes to emerge from participant accounts rather than fitting data to preexisting theoretical frameworks. Results: The following four themes were identified: (1) Conceptualizing Loneliness: practitioners distinguished between social contact and meaningful connection; (2) Contextual Causes: loneliness emerged from life transitions, stigmatized identities, and resource reduction (eg, youth services closures and social support); (3) Expressions and Language: clients rarely expressed loneliness directly, instead using proxy terms, with disclosure patterns varying by age; and (4) Mental Health Co-occurrence: severe mental health conditions created bidirectional cycles of loneliness, exacerbated by symptoms of mental health difficulties. Practitioners reported that many clients experienced loneliness concerns, yet direct disclosure was absent across all participants’ experiences. Conclusions: Practitioners identified multiple stigmatizing experiences as contextual drivers of loneliness, particularly demonstrating how loneliness emerges not only from individual experiences but from broader patterns of social exclusion and marginalization. For therapeutic practice, these insights suggest that practitioners can use awareness of stigmatizing experiences as potential indicators when assessing loneliness risk. The presence of contextual patterns was consistent across practitioners’ experiences, providing a foundation for developing more targeted interventions to address both the emotional experience of loneliness and the underlying social drivers.
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Exercise interventions are most consistently supported for depressive disorders: an umbrella review of diagnosed depressive and anxiety disorders

BackgroundExercise is increasingly discussed as part of lifestyle-based and multimodal care for mood and anxiety disorders, but review-level evidence often mixes formally diagnosed clinical populations with symptom-defined or medically mixed samples.MethodsWe conducted an umbrella review of systematic reviews, meta-analyses, and network meta-analyses of structured exercise interventions for adults with depressive or anxiety disorders. Six databases were searched from inception to 1 March 2026. Primary outcomes were depressive and anxiety symptom severity, remission, and response; secondary outcomes were acceptability and tolerability. Review quality was appraised with AMSTAR 2, and primary-study overlap was quantified with corrected covered area (CCA), including overall and symptom-cluster analyses. The synthesis was designed to summarize review-level credibility and clinical interpretability rather than to generate a second-order pooled efficacy estimate.ResultsNine reviews met eligibility criteria; four supplied directly extractable primary overall review-level estimates for core psychiatric symptom outcomes. AMSTAR 2 appraisal rated one review as high, three as low, and five as critically low. Recalculated overall overlap was slight (112 primary-study occurrences, 89 unique primary studies; CCA = 3.23%), although cluster-level analyses identified localized redundancy, particularly within anxiety-disorder-specific reviews. In major depressive disorder, one clinically focused review reported a large reduction in depressive symptoms for aerobic exercise versus non-exercise comparators (Hedges’ g = -0.79, 95% CI -1.00 to -0.57; I² = 21%). Across diagnosed depressive and/or anxiety disorders, broader review-level estimates also favored exercise for depressive symptoms (SMD = -0.97, 95% CI -1.28 to -0.66) and anxiety symptoms (SMD = -0.66, 95% CI -1.09 to -0.23), but heterogeneity was high. Anxiety-disorder-specific evidence was less secure: the primary DSM-IV anxiety-disorder pooled estimate showed no clear benefit over selected controls (SMD = 0.02, 95% CI -0.20 to 0.24). Acceptability estimates were close to null, and adverse-event reporting was too sparse to support confident safety conclusions.ConclusionExercise is best supported as an adjunctive, patient-centered component of care for depressive disorders. Anxiety-disorder-specific efficacy remains uncertain when comparator rigor, diagnostic heterogeneity, and localized overlap are considered, and safety reporting needs substantial improvement.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD420261364264.
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Prediction of Clinically Significant Depressive Symptoms at 2-Year Follow-Up in Older Adults: Machine Learning Study Using the English Longitudinal Study of Ageing

Background: Depression in older adults is often underdiagnosed due to atypical symptom presentation and generational stigma, leading to delayed intervention. Early identification of individuals at risk of developing elevated depressive symptoms is therefore critical, but traditional approaches show limited predictive accuracy. To date, no study has applied machine learning (ML) models to predict clinically significant depressive symptoms at 2-year follow-up in older adults in the United Kingdom using data from the English Longitudinal Study of Ageing (ELSA). Moreover, the impact of encoding strategies for categorical health care variables has not been examined. Objective: This study aimed to develop and evaluate ML models to predict the clinically significant depressive symptoms at 2-year follow-up in older adults using ELSA data. We further compared ordinal and one-hot encoding strategies across different ML architectures and identified key predictors of depressive symptoms at follow-up. Methods: Data were drawn from 4 consecutive waves of ELSA, including participants aged ≥50 years without significant depressive symptoms at the baseline wave (waves 6‐9). Clinically significant depressive symptoms were defined as 8-item Center for Epidemiologic Studies Depression Scale (CES-D 8) scores of ≥4 at the subsequent wave (waves 7‐10). Over 120 features spanning sociodemographic, psychological, and health-related domains were analyzed. Eight ML models were applied, including tree-based ensembles, deep learning architectures for tabular data, distance-based methods, probabilistic methods, and linear methods. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC) and -score. Model interpretability was examined using Shapley additive explanations (SHAP). Sensitivity analyses assessed the robustness of results across alternative CES-D 8 thresholds (≥3, ≥4, and ≥5) and encoding strategies. Results: Across waves, the best-performing models achieved mean AUROC scores of 0.72‐0.73, with a peak of 0.75 in the highest-performing wave. Ordinal encoding consistently outperformed one-hot encoding across all ML models, yielding improvements in AUROCs and -scores, with the greatest increase in tree-based methods. SHAP consistently identified loneliness, sleep disturbances, and low social engagement as strong predictors of elevated depressive symptoms at follow-up. Sensitivity analyses across CES-D 8 thresholds demonstrated robust feature importance, with AUROCs ranging from 0.67 to 0.82. Traditional ML models (random forest, extreme gradient boosting, and support vector machines) generally achieved higher performance than the deep learning models for this task. Conclusions: Our findings demonstrate the feasibility of predicting clinically significant depressive symptoms at 2-year follow-up in UK older adults, with moderate accuracy. Ordinal encoding demonstrates superior performance for health care datasets with inherently ordered categorical features. The identification of consistent risk factors highlights opportunities for developing targeted clinical screening tools and preventive interventions. This study provides new evidence on depressive symptom prediction in the UK context, leveraging longitudinal data from ELSA, and contributes to advancing digital mental health research for aging populations.
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A Recap of the Inaugural Youth Mental Health Hub at SXSW London

In early June, SXSW London returned for its second year, gathering thousands of creatives, enthusiasts, entrepreneurs, and investors into the city to celebrate film, music, tech, and culture. As part of this year’s festival, the Child Mind Institute, in partnership with Wellcome, proudly presented the inaugural Youth Mental Health Hub – a week of programming dedicated to advancing solutions to one of the defining challenges of our time: the global youth mental health crisis. Through six thought-provoking sessions, leaders in clinical care, science, technology, policy, and media came together to explore how to strengthen prevention, improve early identification, reduce stigma, and build systems that meet young people where they are.

Here’s a look back at the inspiring conversations that took place throughout the week.

Beyond the Average: Understanding Vulnerability in the Digital Childhood Era

Beyond the Average: Understanding Vulnerability in the Digital Childhood Era

As artificial intelligence rapidly transforms the experience of childhood, experts explored how AI can both support and challenge young people’s mental health. The panelists discussed when and under what conditions young people may be most vulnerable as well as what systems we need to support them.

Moderator

Gary Wilson, Director of Research, Huo Family Foundation

Speakers
Catherine Sebastian, PhD, Head of Evidence for Mental Health, Wellcome
John Pickavance, PhD, Principal Data Scientist, Born in Bradford
Georgia Turner, Postdoctoral Research Associate, University of Cambridge
Michael Milham, MD, PhD, Chief Science Officer, Child Mind Institute

AI Is Already Shaping Childhood. Who Is Shaping AI? Balancing Innovation, Evidence, and Safety in Youth Mental Health

AI Is Already Shaping Childhood. Who Is Shaping AI? Balancing Innovation, Evidence, and Safety in Youth Mental Health

Youth are experiencing the impacts of AI earlier and more intensely than any previous generation has. This session explored the role of public leadership in anticipating harm before it becomes systemic — establishing guardrails, fostering digital resilience, and ensuring that innovation advances hand in hand with youth mental health and well-being.

Moderator
Sarah Aguiar-Borges, PhD, University of Cambridge

Speakers
Julia Gillard, former Prime Minister of Australia; Chair, Wellcome
Kanishka Narayan, UK Minister for AI and Online Safety
Giovanni Salum, MD, PhD, SVP, Global Programs, Child Mind Institute.

Youth Mental Health After Conflict: Healing, Resilience, and Rebuilding Systems

Experts shared insights on the unique mental health challenges facing children affected by war, displacement, and humanitarian crises. This session explored how societies can implement youth-centered systems grounded in prevention and use early identification to position youth mental health as a cornerstone of long-term recovery and resilience.

Moderator
Krupa Padhy, BBC Radio 4

Speakers
Dr. Mark Jordans, professor, Centre for Global Mental Health, King’s College London; Director of Research & Development, War Child
Emma Ferguson, mental health policy and advocacy specialist, UNICEF
Mohamed Ali, Director, Iftin Global

Dyslexia: Changing the Story

In a timely discussion, experts explored how dyslexia is currently understood in society, challenging current language and misperceptions that can impact a child’s confidence and mental health. Through a blend of personal experience and clinical expertise, the conversation focused on the need for evidence-based support and strengths-based approaches to help children and their families thrive.

Moderator
Kate Griggs, Founder, Made By Dyslexia

Speakers
Maggie Aderin, PhD, space scientist & educator; dyslexia advocate
Harold S. Koplewicz, MD, President and Medical Director, Child Mind Institute

Connection Continuum: Preventing Suicide and Combating Loneliness

Suicide is one of the leading causes of death among young people globally. This session gathered community, clinical, and digital leaders to explore what a more connected system of support looks like in practice. The panelists also discussed the important of recognizing warning signs, expanding access to evidence-based care, and prioritizing early intervention to help prevent youth suicide.

Moderator
Krupa Padhy, BBC Radio 4

Speakers
Victoria Hornby, CEO, Mental Health Innovations
Dean Perryman, Empty Chairs
Michael Milham, MD, PhD, Chief Science Officer, Child Mind Institute

Does Mental Health Science Funding Need a New Paradigm in the Age of AI?

With technology evolving faster than the science designed to understand it, experts examined how research, philanthropy, and clinical leaders can work together to build the evidence, safeguards, and infrastructure needed to protect children’s mental health in the digital age.

Moderator
Chelsea Clinton, Vice Chair, Clinton Global Initiative

Speakers
Miranda Wolpert, Director of Mental Health, Wellcome
Margaret Laws, President & CEO, HopeLab
Daria Bukhman, Co-Founder and Chair, Bukhman Philanthropies
Harold S. Koplewicz, MD, President & Medical Director, Child Mind Institute

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