Theorising the arts as a health-promoting behaviour

2026-04-23

Arts engagement—defined as participation in or receptivity to creative practices (e.g. music, visual art, performance, storytelling, cultural activities)—is increasingly positioned within a health behaviour framework, though this remains under-theorised. This Review advances a conceptual argument for classifying arts engagement as a health-promoting behaviour, drawing on health behaviour theory, behavioural science, and public health. It situates this within the evolution of health behaviour models, from rational-choice frameworks (e.g. Health Belief Model) to biosocial, socio-ecological, dual-process, and complex systems approaches. Arts engagement is argued to meet ontological definitions of health-related and health-promoting behaviours (e.g. Behaviour Change Intervention Ontology), as it constitutes an individual behaviour capable of improving health outcomes, intentionally or incidentally.Two critiques are addressed: (i) that framing arts engagement as a health behaviour risks instrumentalisation, and (ii) that it implies uniformly positive effects. The paper responds by conceptualising arts engagement along a continuum from informal, intrinsically motivated everyday practices to structured therapeutic interventions, analogous to other health-promoting behaviours (e.g. physical activity). It further examines mechanisms through which arts engagement functions as a health-promoting behaviour, drawing on experimental, quasi-experimental, and epidemiological evidence demonstrating associations and causal effects across psychological, cognitive, physiological, and social outcomes. Emphasis is placed on studies using advanced causal inference methods (e.g. propensity score matching, fixed effects, g-methods, instrumental variables) and longitudinal modelling (e.g. cross-lagged panel models) to disentangle independent and bidirectional relationships while accounting for confounding and selection bias.A roadmap for policy, intervention, and research is outlined. This includes integrating arts engagement into behavioural surveillance, applying frameworks such as COM-B, and formalising classification within behaviour change ontologies. Future research directions include cross-cultural epidemiology, ecological momentary assessment, biomarker integration, exposome-informed models, and One Health frameworks to capture multi-level, life-course dynamics.

Theorising the arts as a health-promoting behaviour

2026-05-14

Arts engagement—defined as participation in or receptivity to creative practices (e.g. music, visual art, performance, storytelling, cultural activities)—is increasingly positioned within a health behaviour framework, though this remains under-theorised. This Review advances a conceptual argument for classifying arts engagement as a health-promoting behaviour, drawing on health behaviour theory, behavioural science, and public health. It situates this within the evolution of health behaviour models, from rational-choice frameworks (e.g. Health Belief Model) to biosocial, socio-ecological, dual-process, and complex systems approaches. Arts engagement is argued to meet ontological definitions of health-related and health-promoting behaviours (e.g. Behaviour Change Intervention Ontology), as it constitutes an individual behaviour capable of improving health outcomes, intentionally or incidentally.Two critiques are addressed: (i) that framing arts engagement as a health behaviour risks instrumentalisation, and (ii) that it implies uniformly positive effects. The paper responds by conceptualising arts engagement along a continuum from informal, intrinsically motivated everyday practices to structured therapeutic interventions, analogous to other health-promoting behaviours (e.g. physical activity). It further examines mechanisms through which arts engagement functions as a health-promoting behaviour, drawing on experimental, quasi-experimental, and epidemiological evidence demonstrating associations and causal effects across psychological, cognitive, physiological, and social outcomes. Emphasis is placed on studies using advanced causal inference methods (e.g. propensity score matching, fixed effects, g-methods, instrumental variables) and longitudinal modelling (e.g. cross-lagged panel models) to disentangle independent and bidirectional relationships while accounting for confounding and selection bias.A roadmap for policy, intervention, and research is outlined. This includes integrating arts engagement into behavioural surveillance, applying frameworks such as COM-B, and formalising classification within behaviour change ontologies. Future research directions include cross-cultural epidemiology, ecological momentary assessment, biomarker integration, exposome-informed models, and One Health frameworks to capture multi-level, life-course dynamics.

Society for Research on Nicotine and Tobacco Europe Debate: The case for countries adopting a policy of reducing nicotine content of cigarettes and other smoked products to minimal levels

Nicotine & Tobacco Research · 2026-05-05

The GALENOS Upper-Level Mental Health Ontology: Enabling evidence synthesis by representing the domain of mental health and associated interventions

Wellcome Open Research · 2026-04-15

<ns3:p> Background Synthesising mental health evidence is challenging due to fragmentation of the field, a high volume of publications and inconsistent terminology being used. Without a better overview of the evidence, developing evidence-based interventions to improve mental health is challenging. The Global Alliance for Living Evidence on aNxiety, depressiOn and pSychosis (GALENOS) addresses these challenges by synthesising and regularly updating evidence through living systematic reviews on anxiety, depression and psychosis. This project is developing a precise classification framework, an ontology, to organise the evidence from these reviews in a data repository. This paper presents the ontology’s upper-level structure, which specifies, defines and links the classes that will organise the data identified in systematic reviews into a shared framework with a common language. Methods The development of the upper-level ontology followed five steps: (1) specifying the ontology’s scope; (2) drafting classes and relationships by reusing parts of existing ontologies and creating new classes where necessary; (3) performing a stakeholder consultation incorporating feedback from mental health experts and people with lived experience; (4) specifying relationships between classes; and (5) making the ontology machine readable and publishing it online. Results The ontology’s upper level includes 25 key classes covering research studies, human populations (including population history, persons and their personal histories), mental health interventions (encompassing both content and delivery) and intervention scenarios, which represent that interventions are delivered in specific contexts with varying plans, outcomes, mechanisms of action and levels of participant engagement. The classes are connected by nine relationship types such as <ns3:italic>participates_in</ns3:italic> and <ns3:italic>has_part.</ns3:italic> Conclusion The upper-level ontology provides a structured framework to represent key aspects of mental health and mental health interventions, supporting linking data and integrating evidence. It also lays the basis for a more elaborated ontology of mental health and interventions. </ns3:p>

Theorising the arts as a health-promoting behaviour

PsyArXiv (OSF Preprints) · 2026-05-11

Arts engagement—defined as participation in or receptivity to creative practices (e.g. music, visual art, performance, storytelling, cultural activities)—is increasingly positioned within a health behaviour framework, though this remains under-theorised. This Review advances a conceptual argument for classifying arts engagement as a health-promoting behaviour, drawing on health behaviour theory, behavioural science, and public health. It situates this within the evolution of health behaviour models, from rational-choice frameworks (e.g. Health Belief Model) to biosocial, socio-ecological, dual-process, and complex systems approaches. Arts engagement is argued to meet ontological definitions of health-related and health-promoting behaviours (e.g. Behaviour Change Intervention Ontology), as it constitutes an individual behaviour capable of improving health outcomes, intentionally or incidentally. Two critiques are addressed: (i) that framing arts engagement as a health behaviour risks instrumentalisation, and (ii) that it implies uniformly positive effects. The paper responds by conceptualising arts engagement along a continuum from informal, intrinsically motivated everyday practices to structured therapeutic interventions, analogous to other health-promoting behaviours (e.g. physical activity). It further examines mechanisms through which arts engagement functions as a health-promoting behaviour, drawing on experimental, quasi-experimental, and epidemiological evidence demonstrating associations and causal effects across psychological, cognitive, physiological, and social outcomes. Emphasis is placed on studies using advanced causal inference methods (e.g. propensity score matching, fixed effects, g-methods, instrumental variables) and longitudinal modelling (e.g. cross-lagged panel models) to disentangle independent and bidirectional relationships while accounting for confounding and selection bias. A roadmap for policy, intervention, and research is outlined. This includes integrating arts engagement into behavioural surveillance, applying frameworks such as COM-B, and formalising classification within behaviour change ontologies. Future research directions include cross-cultural epidemiology, ecological momentary assessment, biomarker integration, exposome-informed models, and One Health frameworks to capture multi-level, life-course dynamics.

TMIC-89. Spatial transcriptomics reveals tumor–brain expression convergence and identifies prognostic gene signatures in breast cancer brain metastases

Neuro-Oncology · 2025-11-01

Abstract BACKGROUND As cancer therapies improve and patients live longer, the incidence of brain metastases (BrMets) continues to rise. Among women with metastatic breast cancer, 10-15% develop BrMets, with rates as high as 30% in HER2+ and 50% in triple-negative. BrMets, represent highly evolved, therapy-resistant tumors with a median survival of 10 months and accompanied by severe neurologic decline. BrMets often emerge years after initial diagnosis, suggesting that metastatic cells must acquire brain-specific traits to survive. This process may involve transcriptional convergence—where tumor cells adopt brain-like programs, and brain-resident cells exhibit tumor-associated changes. Despite its clinical relevance, the molecular basis of this convergence remains poorly defined. Decoding, and targeting, these shared gene programs could reveal new therapeutic vulnerabilities. METHODS We performed spatial transcriptomic profiling on 235 patient tissue cores (BrMets, adjacent normal brain, primary breast tumors, and non-cancer brain). Using NanoString GeoMx, DSP, we measured the expression of 18,677 RNAs across 450 spatially-defined regions enriched for tumor, immune, and brain-resident cells. To interrogate transcriptional convergence, we developed the Equivalent Expression Index, a novel statistical tool to detect genes with biologically meaningful expression similarity across distinct cell populations. RESULTS Our integrated analysis revealed two prognostic gene signatures. The Metastasis-Induced Brain Shared (MIBS-9) signature, comprising brain-like genes upregulated in tumor cells, was associated with longer patient survival. Conversely, the Adjacent brain-Resident Cancer Shared (ARCS-81) signature, defined by tumor-like gene expression in brain-resident cells, correlated with shorter survival. These signatures were externally validated and enriched for pathways related to neuronal remodeling, immune evasion, cellular communication, and cancer/metastasis. CONCLUSION By coupling spatial transcriptomics with a novel equivalence-based algorithm, we uncover distinct, clinically relevant gene programs that mediate bidirectional adaptations between tumor and brain-resident cells. These findings illuminate key mechanisms of metastatic outgrowth and offer new targets for disrupting the breast cancer BrMet microenvironment.

From vectors to symbols to cognition: The symbolic and sub-symbolic aspects of vector-symbolic cognitive models

2025-09-02

To achieve a full, theoretical understanding of a cognitive process, explanations of the process need to be provided at both symbolic (i.e., representational) and sub-symbolic levels of description. We argue that cognitive models implemented in vector-symbolic architectures (VSAs) intrinsically operate at both of levels and thus provide a needed bridge. We characterize the sub-symbolic level of VSAs in terms of a small set of linear algebra operations. We characterize the symbolic level of VSAs in terms of cognitive processes, in particular how information is represented, stored, and retrieved, and classify vector-symbolic cognitive models in the literature according to their implementation of these processes. On the basis of our analysis, we speculate on avenues for future research, and suggest means for theoretical unification of existent models.

Review of: "The Potential of Conversational AI for Mental Health Support in UK Armed Forces Personnel and Veterans: A Mixed-Methods Health Needs Assessment and Feasibility Study"

2025-08-19

This is an interesting and well-written paper on an important topic.It reports a well-conducted study whose strengths and limitations are, in my view, fairly described.The tentative conclusions are worth reporting and following up: primarily that there may be reasonable acceptance of the use of AI chatbots to support mental health in UK armed forces personnel and that, contrary to common expectations, stigma is likely to be less of a deterrent to seeking support than practical issues.The conclusions are expressed with an appropriate level of con dence given the small and likely nonrepresentative sample and the limitations of survey and self-report methodologies.However, they are useful and merit further exploration.I do not have any speci c recommendations for revisions except for the authors to consider linking the research aims and ndings to wider theories and models of help-seeking behaviour and perhaps provide a comment on how far their ndings support or possibly tend to discon rm such models.

Annotating datasets in behavioural and social sciences to promote interoperability: development of the Schema for Ontology-based Dataset Annotation (SODA) version 1.0

Wellcome Open Research · 2025-08-20 · 2 citations

<ns3:p>Background and aims Ontologies are increasingly employed to help find, use and synthesise information, but methods for using them to annotate documents and datasets remain in their infancy in the behavioural and social sciences. The Behavioural Research UK DEMO-DATA project aimed to develop a prototype schema for annotating datasets in behavioural and social sciences. Methods A case-study dataset (the ‘Smoking Toolkit Study’), used to inform an Agent-Based Model of trajectories in cigarette smoking and cessation in England, was chosen for annotation using two ontologies - The Behaviour Change Intervention Ontology (BCIO) and the Addiction Ontology (AddictO). The data set included 21 variables representing information about sociodemographic and tobacco and nicotine use attributes of the study population. A preliminary version of the schema for linking variables to ontology classes was developed as a basis for annotating each variable in the dataset. This was applied and revised iteratively until it was judged by an expert panel of domain experts and modellers to represent the variables sufficiently accurately to enable searching for and integration of data. Results The prototype Schema for Ontology-based Dataset Annotation (SODA) version 1.0 was developed over seven iterations. Variables were represented by an ‘object property’|‘ontology class’ expression (e.g., ‘has characteristic’|‘extent of social smoking’) together with information about the data types (e.g., numbers, ontology subclasses, or Boolean values), measurement source, unit of measurement, any coding or data transformations and whether or not the variable was fully characterised by the annotation. The prototype schema was applied successfully to the smoking dataset with 15 new ontology classes being created as required. Conclusions A prototype schema for annotating behavioural and social science datasets was developed and successfully applied to a dataset on smoking in England using ontology relations and classes. The next step is to further develop and evaluate the schema by application to case studies with a range of users and other datasets.</ns3:p>

A Method for Evaluating the Interoperability of Ontology Classes in the Behavioural and Social Sciences

Wellcome Open Research · 2025-09-22 · 1 citations

<ns3:p>Background Ontologies are frameworks for representing information that promote clarity, consistency and coherence, reduce the fragmentation of knowledge, and allow datasets and knowledge to be linked across studies, disciplines and domains. To enable this, it is important to identify how concepts of interest (‘classes’) are represented in different ontologies and evaluate the extent to which such classes align (i.e., are ‘interoperable’). This study aims to provide a method for doing this. Methods An automated tool using Meta’s Llama 3 language model was developed and used to compare artificial intelligence (AI) and human approaches to matching ontology classes. The automated tool was then integrated into a hybrid method for identifying classes that appear to refer to the same thing across pairs of ontologies. The method was evaluated by three behavioural scientists who used it to identify similar classes in two ontologies and provided feedback on their experience. Results The automated tool identified a larger number of potential matches than human-led review, so was used to generate a shortlist. The evaluation of the method produced mixed results. Users agreed which classes were identical or essentially the same across contexts, but none of the users identified similar classes that could be imported into an ontology without causing a contradiction or conflict. Users typically found using the method difficult, but many of the challenges related to using ontologies, rather than to the method specifically. Conclusions A combination of automated and human processes appears to be a feasible way to assess the interoperability of ontology classes. While further refinement is needed along with tools and resources that enable the use of ontologies by a broad range of researchers, the study provides a workable method for matching ontology classes in the behavioural and social sciences and offers a practical guide to support its implementation.</ns3:p>