Title: A new resource for behavioural science - Developing tools for understanding the relationship between behaviours
Funder: Economic and Social Research Council

Abstract: The overarching aim of the proposed research is to capitalise on the burgeoning body of data available on the relationships between behaviours (e.g. alcohol consumption, political behaviours, sleep, domestic violence, recycling) to (i) develop our understanding of how different types of behaviour are related and (ii) make that information available to behavioural scientists and key stakeholders in a flexible and easy-to-access format. Although there are individual studies that examine these (and related) questions, the community currently lacks a shared understanding of behaviour (e.g. how to describe and differentiate behaviours) and the ability to integrate insights on the relations between behaviour across the extant data. This overarching aim will be achieved via the following objectives: OBJECTIVE 1: DATA STRUCTURE: To develop an ontology that can be used to describe and differentiate behaviours. This will take the form of a 'collaboratively edited ontology' that users will be able to contribute to (similar to Wikidata, an open knowledge base - technology used to store complex structured and unstructured information - that can be read and edited by both humans and machines, https://www.wikidata.org). OBJECTIVE 2: DATA CAPTURE: To create a set of tools (termed a 'collaborative workbench') that people can use to add data on the relationship between behaviours. We will start to populate this knowledge base, but the tools will allow the research community to add data. For example, a researcher who conducts a study that measures aspects of diet (e.g. fruit and vegetable consumption) along with levels of antisocial behaviour would be able to add the correlation between these behaviours (along with other aspects of the study, such as the nature of the sample) to the knowledge base. This (perhaps counterintuitive) examples, speaks to what we would like to achieve - that is, although fruit and vegetable consumption and antisocial behaviour are not obviously linked, a relationship is possible (e.g. fruit and vegetable intake is associated with cognitive function that, in turn, is associated with antisocial behaviour) and if robustly identified would support dietary approaches to reducing antisocial behaviour (for an example, see Benton, 2007, Neuroscience & Biobehavioral Reviews). OBJECTIVE 3: DATA ANALYSIS: To integrate this data in the form of a dynamic, open access knowledge base that people can use to conduct their own reviews by querying the knowledge base. This would allow researchers to examine the likely effects of, for example, reducing alcohol consumption and so query the knowledge base to find out the typical relationship between alcohol consumption and other behaviours such as domestic violence, activity levels, and sleep among UK samples. We will be clear that the dataset represents correlations between behaviours and so should not be taken to necessarily indicate causation (e.g. that an association between alcohol consumption and domestic violence does not imply that one causes the other); however, we will use the Bradford Hill criteria (Hill, 1965) as operationalised by Boniface, Scannell, and Marlow (2017) to estimate the likelihood that a causal relation exists for a given relationship. OBJECTIVE 4: DATA VISUALIZATION: To output the findings of this analysis in the form of a 'clickable' (i.e. interactive) network diagram that expands detail. For example, a user could click on 'physical activity' to open subtypes such as walking, running etc., followed by further subtypes such as walking the dog, walking to the shops etc. along with the associations between behaviours at each level and other nodes (behaviours) in the ontology. This will allow people to express the complexity of their studies and also filter the data according to their criteria (e.g. if interested in walking for exercise, rather than as a mode of transport).

Lay abstract: Do people who go to bed earlier and sleep for longer exercise more during the day? Is driving behaviour associated with whether people recycle or help other people? What if someone carries a reusable coffee cup? Are they more likely to conserve biodiversity at home (e.g. put up a bird box)? Some associations between behaviours are intuitive (e.g. people who are more physically active may sleep longer, either because they view both as 'healthy' behaviours or because active people need more rest), while others are less intuitive (e.g. relations between driving behaviour and efforts to conserve biodiversity). However, psychologists (and other behavioural scientists) often view behaviours in isolation - seeking to, for example, improve sleep or increase levels of physical activity. Sometimes these sorts of studies will consider the extent to which changes in one behaviour 'spillover' into changes in another (e.g. increasing physical activity leads people to also make more healthy food choices) or lead to compensation (e.g. increasing physical activity leads people to consume more calories). However, these analyses are typically only limited to a small number of behaviours, usually within the same domain (e.g. health or environmental behaviours). There are also questions about how behaviours are defined and operationalised (e.g. what constitutes an increase in physical activity?), pointing to the need for agreed definitions and/or a framework that permit comparisons between studies. In short, given that everyday life is characterized by a wide range of behaviours, it is crucial to understand how behaviours are related to one another, both within and across domains, both to develop our understanding of behaviour and to inform interventions. Fortunately, a lot of evidence needed to understand the relationships between behaviours already exists. Any study that measures two or more behaviours and reports the correlation between them, or that provides access to data that allows the correlation to be calculated, can provide an estimate of their relationship, which can be pooled across datasets. However, reviews to date lack a framework for defining behaviours and have tended to rely on relatively complex ways of looking at the relations between behaviours (e.g. cluster and network analysis), which can make the findings difficult to interpret and have been limited to considering the relations between behaviours within domains (e.g. health), making it difficult to understand whether and how, for example, people make tradeoffs between domains. Our proposal is to develop tools that will allow behavioural scientists to define behaviours, along with their similarities and differences, by creating a structured model (e.g. that drinking alcohol and taking cocaine are both examples of substance use, but only using cocaine is illegal). We will then start to collate data on the relationship between behaviours (e.g. from published papers, large secondary datasets) and develop a set of tools - termed a "collaborative workbench" - that will allow researchers to enter their own information to enable easy, rapid, and efficient generation of new knowledge. Finally, we will develop ways to visualize the data and allow users (e.g. academics, policy makers, stakeholders) to pose questions to the community and to query the knowledge base to provide robust answers to questions about how behaviours are related.

Research activity codes: 1.4 METHODOLOGIES AND MEASUREMENTS
Health categories: GENERIC HEALTH RELEVANCE