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      Smart food policies for obesity prevention.

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          Abstract

          Prevention of obesity requires policies that work. In this Series paper, we propose a new way to understand how food policies could be made to work more effectively for obesity prevention. Our approach draws on evidence from a range of disciplines (psychology, economics, and public health nutrition) to develop a theory of change to understand how food policies work. We focus on one of the key determinants of obesity: diet. The evidence we review suggests that the interaction between human food preferences and the environment in which those preferences are learned, expressed, and reassessed has a central role. We identify four mechanisms through which food policies can affect diet: providing an enabling environment for learning of healthy preferences, overcoming barriers to the expression of healthy preferences, encouraging people to reassess existing unhealthy preferences at the point-of-purchase, and stimulating a food-systems response. We explore how actions in three specific policy areas (school settings, economic instruments, and nutrition labelling) work through these mechanisms, and draw implications for more effective policy design. We find that effective food-policy actions are those that lead to positive changes to food, social, and information environments and the systems that underpin them. Effective food-policy actions are tailored to the preference, behavioural, socioeconomic, and demographic characteristics of the people they seek to support, are designed to work through the mechanisms through which they have greatest effect, and are implemented as part of a combination of mutually reinforcing actions. Moving forward, priorities should include comprehensive policy actions that create an enabling environment for infants and children to learn healthy food preferences and targeted actions that enable disadvantaged populations to overcome barriers to meeting healthy preferences. Policy assessments should be carefully designed on the basis of a theory of change, using indicators of progress along the various pathways towards the long-term goal of reducing obesity rates.

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          Most cited references131

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          Is Open Access

          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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            The global obesity pandemic: shaped by global drivers and local environments

            The Lancet, 378(9793), 804-814
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              The spread of obesity in a large social network over 32 years.

              The prevalence of obesity has increased substantially over the past 30 years. We performed a quantitative analysis of the nature and extent of the person-to-person spread of obesity as a possible factor contributing to the obesity epidemic. We evaluated a densely interconnected social network of 12,067 people assessed repeatedly from 1971 to 2003 as part of the Framingham Heart Study. The body-mass index was available for all subjects. We used longitudinal statistical models to examine whether weight gain in one person was associated with weight gain in his or her friends, siblings, spouse, and neighbors. Discernible clusters of obese persons (body-mass index [the weight in kilograms divided by the square of the height in meters], > or =30) were present in the network at all time points, and the clusters extended to three degrees of separation. These clusters did not appear to be solely attributable to the selective formation of social ties among obese persons. A person's chances of becoming obese increased by 57% (95% confidence interval [CI], 6 to 123) if he or she had a friend who became obese in a given interval. Among pairs of adult siblings, if one sibling became obese, the chance that the other would become obese increased by 40% (95% CI, 21 to 60). If one spouse became obese, the likelihood that the other spouse would become obese increased by 37% (95% CI, 7 to 73). These effects were not seen among neighbors in the immediate geographic location. Persons of the same sex had relatively greater influence on each other than those of the opposite sex. The spread of smoking cessation did not account for the spread of obesity in the network. Network phenomena appear to be relevant to the biologic and behavioral trait of obesity, and obesity appears to spread through social ties. These findings have implications for clinical and public health interventions. Copyright 2007 Massachusetts Medical Society.
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                Author and article information

                Journal
                Lancet
                Lancet (London, England)
                1474-547X
                0140-6736
                Jun 13 2015
                : 385
                : 9985
                Affiliations
                [1 ] World Cancer Research Fund International, London, UK. Electronic address: c.hawkes@wcrf.org.
                [2 ] Department of Economics, University of Otago, Dunedin, New Zealand.
                [3 ] World Cancer Research Fund International, London, UK.
                [4 ] Health Behaviour Research Centre, Department of Epidemiology & Public Health, University College London, London, UK.
                [5 ] Center on Social Dynamics and Policy, The Brookings Institution, Washington, DC, USA.
                [6 ] Regulatory Institutions Network, The Australian National University, Canberra, ACT, Australia.
                [7 ] Menzies Centre for Health Policy, University of Sydney, Sydney, NSW, Australia.
                [8 ] Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile.
                Article
                S0140-6736(14)61745-1
                10.1016/S0140-6736(14)61745-1
                25703109
                9e29cd46-aa15-4437-8547-a1c42b8a4ca9
                Copyright © 2015 Elsevier Ltd. All rights reserved.
                History

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