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Exploring the use of interactive data dashboards as a tool to support a data-driven approach to whole-school health improvement: case studies from the DATAMIND project in Wales and Scotland
No full textSchool is an important setting for supporting young people's healthy development and positive mental wellbeing. Recent curriculum changes in Scotland and Wales reflect this, adopting a whole- school approach to health and wellbeing as a central pedagogical focus and responsibility of all working in the sector. Alongside education system reform, there is a growing recognition of the need for health improvement decision- making in schools to take a data- driven approach. Interactive data dashboards are data visualisation tools that can facilitate quick information processing and effective decision- making. The Schools Health and Wellbeing Improvement Research Network (SHINE) in Scotland and the School Health Research Network (SHRN) in Wales have been exploring interactive data dashboards as tools for sharing health and wellbeing data with teachers, pupils and parents. This paper explores the views of Local Authority staff, teachers and pupils on the potential application of interactive data dashboards within the school setting to inform health improvement planning. Findings suggest that these offer an accessible tool that could promote cross- curricular health and wellbeing learning, strengthen the link between home and school life, and engage education and community partners in data- informed health and wellbeing promotion. However, the needs of the school community must remain central in the dashboard design process. Implications for health and wellbeing data- related practices within the school community and future directions are discussed
Time to unlearn unlearning?: Moving beyond neoliberal conceptualizations to intersectional, transformative actions
No full textUnlearning, often defined as the process of critically examining one's beliefs and assumptions in relation to societal roles and privileges (Britzman, 1998; Cochran-Smith, 2000), has gained traction in anti-racist, decolonial, and social justice-oriented discourses. Despite its growing popularity, limited research has explored how—or even whether—unlearning actually occurs, and whether it extends beyond individual introspection to enable broader social transformation. This study draws on the experiences of 17 language teachers with diverse identities in terms of race, gender, sexuality, class, and neurodiversity to critically investigate the how (process), what (object), and why (impact) of unlearning. Findings suggest that unlearning, as currently framed, often functions as a depoliticized, neoliberal concept grounded in individual confession, rather than in collective action. This research proposes a renewed conceptualization through an intersectional and transformative approach that challenges systemic structures and moves beyond passive allyship toward genuine advocacy and activism centred on community, cooperation and collective responsibility
Exhaled breath acetone: a non-invasive marker of disease severity across the spectrum of heart failure
No full textBackground. Increased exhaled breath acetone (EBA) concentrations might reflect impaired myocardial energetics and haemodynamic stress. We investigated the relation of EBA and cardiac structure, function, and exercise capacity in patients with or at risk of heart failure (HF). Methods. We enrolled outpatients with HF and reduced (<50%, HFrEF) or preserved (>50%, HFpEF) left ventricular ejection fraction (LVEF) and subjects with cardiovascular risk factors and/or structural heart disease without established HF. All participants underwent clinical and laboratory evaluation, resting transthoracic echocardiography, and a combined cardiopulmonaryechocardiographic stress test with EBA monitoring at rest (EBAᵣₑₛₜ) and during exercise (EBAₑₓ). Results. Patients with HFpEF (n = 62) were older and more often female than those at risk of HF (n =50)or with HFrEF (n =41). EBAᵣₑₛₜ (1.5, interquartile range (IQR) 1.0–3.1 vs 0.9, IQR 0.71.2 mcg l⁻¹) and EBAₑₓ (2.4, IQR 1.5–4.4 vs 1.1, IQR 0.9–2.1 mcg l⁻¹; all p < 0.0001) were significantly higher in patients with HF compared to others. Among HF patients, those in the highest EBAᵣₑₛₜ tertile had lower LVEF, greater echocardiographic signs of congestion, higher NT-proBNP levels, and lower peak oxygen consumption, indicating impaired exercise capacity. In multivariate regression, NT-proBNP (p = 0.0004) and the slope of minute ventilation to carbon dioxide production (p = 0.0013) were independent predictors of EBAᵣₑₛₜ (adjusted R² = 0.458). Conclusions. EBAconcentrations are higher in patients with HF compared to those without, regardless of LVEF, and are associated with markers of disease severity. Further studies are needed to determine whether EBA measurement can aid in HF diagnosis and management
A comprehensive approach for Bayesian soil classification using Cone Penetration Test data
No full textClassification is a critical soil characterisation task, meaning there is considerable value in methods which can accurately and reliably predict soil type from cost-effective Cone Penetration Test (CPT) data. Traditional methods involve plotting measurements against 2-dimensional charts. However, these have evolved only incrementally over past decades despite several key limitations: (i) inference in two dimensions is suboptimal, as more than two measurements are available; (ii) the quantitative specification of the charts is relatively untransparent; and (iii) the outputs are deterministic, which can be misleading as the estimation process is highly uncertain. This paper introduces a comprehensive CPT-based classification approach which is entirely data-driven and delivers probabilities for each possible soil type. Reliable training data is curated from a large database through automated pairing of laboratory and CPT data, with the latter subject to novel pre-processing techniques to remove uncharacteristic features. Bayesian inference is used to characterise a multinomial logistic regression model: given training data and an optional prior model density, the posterior density is computed. Class prediction for new data is then performed through Monte Carlo integration over the posterior density. The resulting model is found to deliver strong classification accuracy, and the probabilistic output is highly practical for interpretation; in particular, being well suited to automatic stratification. Additionally, an existing model can easily be adapted to a new set of classes and re-trained, enabling refinement or site-specific calibration, with robust continuity facilitated through use of the prior
A hybrid offline-online model order reduction approach for damage propagation problems
Get PDFAccurately modelling damage propagation in composites with 3D explicit finite element methods requires high-dimensional models, making simulations computationally prohibitive. Conventional reduced-order models (ROMs) trained offline are ineffective for fracture problems, since stress redistribution and crack growth cannot be anticipated a priori. In this work, a hybrid offline–online ROM that couples elastic-only offline training with adaptive online enrichment of the reduced basis during damage evolution is introduced. Proper Orthogonal Decomposition (POD) is combined with Energy-Conserving Mesh Sampling and Weighting (ECSW) and Gappy data reconstruction to achieve efficient time integration with 3D solid and cohesive elements. Unlike existing domain decomposition approaches, the proposed framework does not require prior knowledge of crack paths and can refine the basis anywhere in the domain as damage develops. The method is demonstrated on open-hole tensile tests at two distinct length scales, capturing delamination, fibre failure and failure stress with good accuracy when compared to full-order simulations and experiments. Improved computational savings are achieved, with efficiency gains increasing with model size. These results establish the hybrid ROM as a scalable and general approach for modelling distributed, path-dependent fracture in composite materials
Obesity, aldosterone, and natriuretic peptide in patients with heart failure and reduced ejection fraction
Get PDFAims: Experimental evidence suggests that adipose tissue may secrete aldosterone, and mineralocorticoid receptor antagonists (MRAs) appear to be more effective in patients with obesity. Therefore, we examined aldosterone levels according to measures of adiposity in patients with heart failure and reduced ejection fraction (HFrEF) participating in two large trials.
Methods and results: Aldosterone, N-terminal pro B-type natriuretic peptide (NT-proBNP), and B-type natriuretic peptide (BNP) levels were compared according to body mass index (BMI) categories: normal weight (<25.0 kg/m2), overweight (25.0–29.9 kg/m2), obesity class I (30.0–34.9 kg/m2), and obesity class II (≥35.0 kg/m2). Of the 2,201 patients not treated with an MRA, in whom aldosterone levels were measured at baseline in ATMOSPHERE and PARADIGM-HF, the mean age was 67.8 years, and 440 (20.0%) were female. Patients with higher BMI had a higher left ventricular ejection fraction but worse New York Heart Association functional class than those with normal weight. Higher BMI was associated with higher aldosterone levels but lower NT-proBNP and BNP levels (P for trend<0.001), compared to those with normal weight. This trend was also seen for other anthropometric measures.
Conclusions: Greater adiposity was associated with higher concentrations of aldosterone but lower levels of B-type natriuretic peptides in patients with HFrEF. Adipose tissue may influence the neurohumoral milieu in HFrEF, including the secretion of aldosterone
Multiscale analysis of electrically stimulated vascularised tumours
Get PDFElectroporation-based therapies such as electrochemotherapy (ECT) hold a great promise for improving cancer treatments. While highly effective for superficial tumours, its application for deep-seated malignancies is challenged by complex microstructural properties, and current models often lack a multiscale theoretical framework to capture those phenomena. Here we develop and solve a novel system of coupled partial differential equations of Darcy-Laplace type obtained by applying the asymptotic homogenisation technique. We study the tumour response stimulated by an electric field. We derive effective macroscale equations for the pressure, velocity, and electric potential, whilst incorporating both hydraulic and electric microscale tissue heterogeneities. Our coupled multiscale approach bridges the gap between the tumour microstructure and macroscale dynamics, offering a more comprehensive understanding of how tumour size, morphology, and hydraulic-electrical interactions influence interstitial flow. We present a parametric analysis of the hydraulic conductivity tensor and macroscale numerical simulation results for pressure and velocity fields, highlighting the role of the electric field in modulating fluid flow. Our findings provide meaningful insights towards advancing ECT protocols
Finite-element Gaussian processes for the machine learning of steady-state linear partial differential equations
No full textWe introduce finite-element Gaussian processes (FEGPs), a novel physics-informed machine learning approach for solving inverse problems involving steady-state, linear partial differential equations (PDEs). Our framework combines a Gaussian process prior for the unknown solution function with a likelihood that incorporates the PDE in its weak form, using a finite-element approximation. This approach offers significantly better scalability than physics-informed Gaussian processes (PIGPs), which rely on the strong form of the PDE. Through numerical experiments on a range of synthetic benchmark problems, we show that FEGPs offer results which outperform PIGPs, and are competitive with physics-informed neural networks (PINNs) with improved uncertainty quantification
"I Want to Keep My Phone Away From the Bed": Designing a Smart Pillow for Sleep Onset
No full textPre-sleep digital consumption is widespread. While it is a common concern for bedtime procrastination, recent research also highlights its importance in fulfilling various pre-sleep needs, such as claiming "me time". However, these benefits and underlying needs have largely been overlooked in the design of digital sleep interventions. In this paper, we present a co-design workshop with 16 participants, exploring a smart pillow that allows audio-based digital consumption through non-distracting interactions. We illustrate the smart pillow’s potential in resolving the tension between digital consumption and sleep transition to support sleep onset — the transition from wakefulness to sleep. Our work highlights how the pillow’s physical form affords audio consumption control with minimal effort, and positions sleep onset as a distinct design context that demands careful attention when designing for sleep. We offer design implications that leverage tangible and bodily interactions to accommodate the sensitive transitioning state during sleep onset
A systematic review and meta-analysis examining the impact of placement instability on the mental health outcomes of care experienced children and young people
Get PDFBackground:
Care experienced children and young people (CECYP) are at risk of mental health difficulties. This review aimed to examine the impact of placement instability on the mental health outcomes of CECYP and to explore how placement instability is measured.
Methods:
This review was conducted following the PRISMA guidance. Four databases (PsycINFO, Embase, Medline and ProQuest) were initially searched on 14th December 2023, with an updated search conducted on January 29th 2025. The following inclusion criteria were used: quantitative observational studies, CECYP (0–18 years), mental health difficulties as the outcome and placement instability as the exposure. The quality of the included studies was also assessed. The results from all papers included in the review were narratively synthesised and results from a subsample of the papers eligible for quantitative synthesis were analysed using a random effects meta-analysis to examine the association between placement instability and mental health outcomes of CECYP.
Results:
Twenty-two studies were eligible for inclusion. The measurement of placement instability varied with some studies counting number of placement moves and some grouping number of moves into levels. The time frames in which moves were measured also varied. Overall, placement instability had a negative impact on mental health outcomes in CECYP, irrespective of age, sex, domain of mental health assessed (internalising and externalising), and initial levels of mental health. The meta-analysis found that placement instability had a small significant association with both internalising (r = 0.14, 95% CI = 0.12, 0.17) and externalising (r = 0.14, 95% CI = 0.11, 0.18) mental health difficulties.
Conclusion:
Placement instability is inconsistently measured and defined in the literature. Despite this heterogeneity in operationalisation, it remains a concerning risk factor for mental health difficulties of CECYP. Efforts should be made to minimise instability for this population.
Trial Registration:
The protocol was registered on PROSPERO on the 2nd of February 2024 (CRD42024444031), can be found at https://www.crd.york.ac.uk/PROSPERO/view/CRD42024444031
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