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

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.

The proposed project aims to create an essential resource for stakeholders that want - or need - to understand how behaviours are related. Below, we detail some specific groups who would benefit from such a resource and how.

Practitioners and policy makers tasked with understanding and changing behaviour. Almost all societal grand challenges, whether concerning the environment, health, well-being, psychological distress, criminal justice, or the development of sustainable economic models, have at their heart a need to understand behaviour; both as a way to promote change and to understand the impact of change on other behaviours. To give some examples, healthcare practitioners want to understand whether vaccination uptake is associated with other health behaviours (e.g. screening), politicians want to understand how their voters (and non-voters) behave, local authorities want to understand how transport use (e.g. the amount that people walk versus drive) is associated with pro-environmental actions like recycling, and policy makers are interested in how legislation targeting a specific behaviour (e.g. a tax on the consumption of sugary drinks) could confer additional benefit (e.g. because consuming sugary drinks is associated with consumption of fast food in general). Understanding these relations could help to identify targets for intervention and target resources, as well as understand how interventions designed to change one behaviour (e.g. promoting vaccination uptake) are associated with changes in other behaviours.

Business and commercial interests will benefit from tools and resources that could be used to understand how behaviours are related. For example, a supermarket that needs to understand whether and how behaviours like reusing coffee cups are associated with other actions, such as avoiding meat, could use the resource to identify the association between these behaviours. A gym tasked with helping users to lose weight would be interested in the extent to which behaviours construed as 'exercise' (e.g. going to the gym, running) compensate for other behaviours that have similar benefits, but are construed differently (e.g. gardening, walking etc.). They could use the resource to look at the relations between these behaviours and target their efforts and resources accordingly (e.g. if there is a relationship, then they could encourage users not to walk less on days that they visit the gym). Finally, a price comparison website might benefit from knowing which behaviours are associated with financial behaviours like switching energy supplier in order to profile their market and target resources accordingly.

Finally, the proposed research will also be of interest to the media who want to communicate stories about how people manage and balance different behaviours within their lives. Our project will help to understand the extent to which behaviours are related within and across domains and can therefore answer questions of public interest such as is do people who work harder necessarily act in less pro-environmental and healthy ways? Which behaviours are associated with spending time with the family? Is playing bingo associated with other forms of gambling? Such media reports will likely have additional impact on those who read them in the sense that they help people to understand how behaviours are related, potentially leading people to reflect on their behaviour and that of those around them.