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      Behaviour Centred Design: towards an applied science of behaviour change

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          ABSTRACT

          Behaviour change has become a hot topic. We describe a new approach, Behaviour Centred Design (BCD), which encompasses a theory of change, a suite of behavioural determinants and a programme design process. The theory of change is generic, assuming that successful interventions must create a cascade of effects via environments, through brains, to behaviour and hence to the desired impact, such as improved health. Changes in behaviour are viewed as the consequence of a reinforcement learning process involving the targeting of evolved motives and changes to behaviour settings, and are produced by three types of behavioural control mechanism (automatic, motivated and executive). The implications are that interventions must create surprise, revalue behaviour and disrupt performance in target behaviour settings. We then describe a sequence of five steps required to design an intervention to change specific behaviours: Assess, Build, Create, Deliver and Evaluate. The BCD approach has been shown to change hygiene, nutrition and exercise-related behaviours and has the advantages of being applicable to product, service or institutional design, as well as being able to incorporate future developments in behaviour science. We therefore argue that BCD can become the foundation for an applied science of behaviour change.

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          Most cited references 114

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          The theory of planned behavior

           Icek Ajzen (1991)
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            Human-level control through deep reinforcement learning.

            The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
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              The behaviour change wheel: A new method for characterising and designing behaviour change interventions

              Background Improving the design and implementation of evidence-based practice depends on successful behaviour change interventions. This requires an appropriate method for characterising interventions and linking them to an analysis of the targeted behaviour. There exists a plethora of frameworks of behaviour change interventions, but it is not clear how well they serve this purpose. This paper evaluates these frameworks, and develops and evaluates a new framework aimed at overcoming their limitations. Methods A systematic search of electronic databases and consultation with behaviour change experts were used to identify frameworks of behaviour change interventions. These were evaluated according to three criteria: comprehensiveness, coherence, and a clear link to an overarching model of behaviour. A new framework was developed to meet these criteria. The reliability with which it could be applied was examined in two domains of behaviour change: tobacco control and obesity. Results Nineteen frameworks were identified covering nine intervention functions and seven policy categories that could enable those interventions. None of the frameworks reviewed covered the full range of intervention functions or policies, and only a minority met the criteria of coherence or linkage to a model of behaviour. At the centre of a proposed new framework is a 'behaviour system' involving three essential conditions: capability, opportunity, and motivation (what we term the 'COM-B system'). This forms the hub of a 'behaviour change wheel' (BCW) around which are positioned the nine intervention functions aimed at addressing deficits in one or more of these conditions; around this are placed seven categories of policy that could enable those interventions to occur. The BCW was used reliably to characterise interventions within the English Department of Health's 2010 tobacco control strategy and the National Institute of Health and Clinical Excellence's guidance on reducing obesity. Conclusions Interventions and policies to change behaviour can be usefully characterised by means of a BCW comprising: a 'behaviour system' at the hub, encircled by intervention functions and then by policy categories. Research is needed to establish how far the BCW can lead to more efficient design of effective interventions.
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                Author and article information

                Journal
                Health Psychol Rev
                Health Psychol Rev
                RHPR
                rhpr20
                Health Psychology Review
                Routledge
                1743-7199
                1743-7202
                1 October 2016
                18 August 2016
                : 10
                : 4
                : 425-446
                Affiliations
                [ a ]Department of Infectious Disease, London School of Hygiene and Tropical Medicine , London, UK
                1219673
                10.1080/17437199.2016.1219673
                5214166
                27535821
                © 2016 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License ( http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.

                Counts
                Figures: 3, Tables: 1, Equations: 0, References: 125, Pages: 22
                Product
                Funding
                Funded by: Wellcome Trust 10.13039/100004440
                Funded by: Unilever 10.13039/100007190
                Funded by: DFID, ESRC, SHARE, UNICEF 10.13039/100006641
                Funded by: World Bank 10.13039/100004421
                Funded by: GoJo Industries, Bill and Melinda Gates Foundation 10.13039/100000865
                Funded by: Global Alliance for Improved Nutrition
                During the development of this approach, the authors have been funded by the Wellcome Trust, Unilever, DFID, ESRC, SHARE, UNICEF, the World Bank, GoJo Industries, the Bill and Melinda Gates Foundation and the Global Alliance for Improved Nutrition. However, none of these funders specifically funded nor have been directly involved in the development of the approach.
                Categories
                Article
                Conceptual Review

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