Generating change through collective impact and systems science for childhood obesity prevention: The GenR8 Change case study

Abstract

Background Community-based interventions have shown promise in reducing childhood overweight and obesity. However, they have been critiqued for using linear logic models. Participatory community-based systems approaches are posited as addressing the complexity of non-linear relationships in a local context. Community members are empowered to understand and describe obesity causation, identify and prioritise possible solutions. The application of such approaches to childhood obesity is in its infancy. Aim To describe the first 12 months of a participatory whole-of-community systems approach to creating collective action to tackle childhood obesity, called GenR8 Change, in a local government area of Victoria, Australia. Methods Three group model building (GMB) sessions focused on the development of a causal loop diagram (CLD), prioritised evidence-informed actions, and developed implementation strategies. The collective impact framework underpinned the approach, with a local backbone group supporting community members to implement prioritised actions. Results The first two GMB sessions included 20 key community leaders where a CLD examining the factors contributing to childhood obesity in the community was constructed and refined (22 variables GMB1, 53 variables GMB2). In the third session, 171 members of the wider community further refined the CLD, identified priorities for childhood obesity prevention (72 variables in final CLD). One-hundred and thirteen individuals signed up across 13 working groups to plan and implement 53 prioritised actions. Agreed community actions included creating sugar free zones; developing healthy policies; increasing breastfeeding rates; improving drinking water access; and increasing physical activity options. Twelve months post-GMB3, 115 actions had been implemented. Conclusion GenR8 Change is one of the first communities to apply systems thinking to childhood obesity prevention. Knowledge on how to collectively identify relevant leverage points to tackle childhood obesity can now be shared with other communities.

The need for a complex systems approach in rural health research

“…Despite growing momentum around complex systems approaches, their specific application to issues contained within rural health has received a lack of attention aside from a few notable exceptions, including systems mapping33–35 and dynamic modelling36 studies. Complex systems approaches may help to identify new rural health solutions, identify key leverage points to address workforce issues such as provider maldistribution and shortage,37 support cost-benefit decision-making, and contribute to the evaluation of existing strategies given competing priorities and the balancing of limited resources.…”

“…1 10 21 23-26 28-32 There is mounting evidence to suggest that a complex systems approach can be of practical assistance in both explaining mechanisms driving adverse health outcomes and system behaviour and also determining where and how to intervene through optimal leverage to achieve positive population health outcomes. 18 23-25 27 Despite growing momentum around complex systems approaches, their specific application to issues contained within rural health has received a lack of attention aside from a few notable exceptions, including systems mapping [33][34][35] and dynamic modelling 36 studies. Complex systems approaches may help to identify new rural health solutions, identify key leverage points to address workforce issues such as provider maldistribution and shortage, 37 support cost-benefit decision-making, and contribute to the evaluation of existing strategies given competing priorities and the balancing of limited resources.…”

“…A CLD is a useful standalone tool for visualising complexity and conceptualising relatively simple behaviours, including how the various parts of a system (eg, agents, factors, processes) interact to explain or create a problem. 48 There are very few examples of CLDs applied specifically in rural contexts, [33][34][35] and further applications are warranted. Despite their holistic point of reference, CLDs are still only static representations and conceptual errors and complex behaviours are often only realised when models are translated into a dynamic format, such as SD modelling.…”

“…In this regard, static systems modelling, such as CLDs or socioecological models, could be a useful starting point to conceptualise complexity and generate a rich picture of the problem and the key agents and factors involved as a means to direct subsequent analyses. [33][34][35] The next step in the research programme might involve the use of a 'top down' computational method, such as SD modelling, to reveal how non-linear system dynamics and behaviours drive change and shape health outcomes, thereby increasing quantitative precision. The third and final phase may drill down further into key parts of the rural health system via the use of an ABM, to understand the complex processes that give rise to health or health system outcomes, although from a 'ground up' perspective that appreciates individual exchanges of information, labour and skill between health professionals and health system managers.…”

Applying the complementary knowledge bases of System Dynamics and Indigenous knowledge in public health research in Aotearoa, New Zealand

“…Systems Thinking is beginning to be applied in public health research on chronic and complex issues such as obesity prevention, improving nutrition and cardiovascular disease (Appel et al, 2019; McGlashan et al, 2018), with examples from Australia (Allender et al, 2019; Bolton et al, 2022; Jenkins et al, 2020) and Aotearoa, New Zealand (Gerritsen et al, 2020; Kenealy et al, 2012). These approaches embrace the complex messiness that arises between different perspectives and stakeholders and provide tools and methods to bring people and perspectives together (Conte & Davidson, 2020).…”