A new resource for behavioural science - Developing tools for understanding the relationship between behaviours

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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.