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ObjectiveTo present an approach to build capacity for the use of systems science to support local communities in municipal public health and well-being planning.DesignCase study.SettingLocal government authorities participating in the VicHealth Local Government Partnership in Victoria, Australia.ParticipantsLocal government staff members were trained in community-based system dynamics (CBSD), and group model building (GMB) techniques to mobilise local community efforts. The trained local government facilitation teams then delivered GMB workshops to community stakeholder groups from 13 local government areas (LGA)s.Main outcomesTraining in CBSD was conducted with council facilitation teams in 13 LGAs, followed by the local delivery of GMB workshops 1–3 to community stakeholders. Causal loop diagrams (CLD) representing localised drivers of mental well-being, healthy eating, active living or general health and well-being of children and young people were developed by community stakeholders. Locally tailored action ideas were generated such as well-being classes in school, faster active transport and access to free and low-cost sporting programmesResultsOverall, 111 local government staff participated in CBSD training. Thirteen CLDs were developed, with the stakeholders that included children, young people and community members, who had participated in the GMB workshops across all 13 council sites. Workshop 3 had the highest total number of participants (n=301), followed by workshop 1 (n=287) and workshop 2 (n=171).ConclusionsLocal facilitation of the CBSD process has developed community informed and locally relevant CLDs that will be used to lead local action to improve the well-being of children and young people. Training employees in CBSD is one approach to increase systems thinking capacity within local government.

Background In Australia, around 67% of adults and 25% of children (5–17 years) are currently overweight or obese (Australian Bureau of Statistics, 4364.0.55.001 - National Health Survey: First Results, 2017–18, 2018). The Campbelltown – Changing our Future study will translate ‘a whole of system’ approach, previously trialed in rural communities in Victoria and the Australian Capital Territoty, to Campbelltown Local Government Area (LGA), a socioeconomically and ethnically diverse urban community in south western Sydney, NSW. Methods The study intervention will use a five-step approach; 1 – set up a childhood obesity monitoring system by collecting baseline data from children in primary schools across Campbelltown LGA to give a local context to the community when developing the systems map; 2 - key stakeholders develop systems maps which inform the development of the interventions; 3 - key stakeholders and community groups identify priority areas for action and form working groups; 4 - implementation of the interventions; 5 - evaluation of the interventions. The study will adopt a longitudinal pre/post design with repeated measures at baseline, 2 years and 4 years. Both qualitative and quantitative methods will be used to collect and analyse the data. Discussion Addressing childhood overweight and obesity is complex and requires a multifaceted intervention. This approach has the capacity to impact a range of factors that influence childhood overweight and obesity utilising existing capacity of multiple partners with broad community reach. Findings will develop local responses which capture the complexity of obesity at a community level and further our understanding of the interrelationships and relative importance of local factors impacting childhood overweight and obesity. This study aims to provide evidence for systems methods and approaches suitable for adaption and scaling and may provide evidence of successful community intervention elements.

IntroductionSystems science methodologies have been used in attempts to address the complex and dynamic causes of childhood obesity with varied results. This paper presents a protocol for the Reflexive Evidence and Systems interventions to Prevention Obesity and Non-communicable Disease (RESPOND) trial. RESPOND represents a significant advance on previous approaches by identifying and operationalising a clear systems methodology and building skills and knowledge in the design and implementation of this approach among community stakeholders.Methods and analysisRESPOND is a 4-year cluster-randomised stepped-wedge trial in 10 local government areas in Victoria, Australia. The intervention comprises four stages: catalyse and set up, monitoring, community engagement and implementation. The trial will be evaluated for individuals, community settings and context, cost-effectiveness, and systems and implementation processes. Individual-level data including weight status, diet and activity behaviours will be collected every 2 years from school children in grades 2, 4 and 6 using an opt-out consent process. Community-level data will include knowledge and engagement, collaboration networks, economic costs and shifts in mental models aligned with systems training. Baseline prevalence data were collected between March and June 2019 among >3700 children from 91 primary schools.Ethics and disseminationEthics approval: Deakin University Human Research Ethics Committee (HREC 2018-381) or Deakin University’s Faculty of Health Ethics Advisory Committee (HEAG-H_2019-1; HEAG-H 37_2019; HEAG-H 173_2018; HEAG-H 12_2019); Victorian Government Department of Education and Training (2019_003943); Catholic Archdiocese of Melbourne (Catholic Education Melbourne, 2019-0872) and Diocese of Sandhurst (24 May 2019). The results of RESPOND, including primary and secondary outcomes, and emerging studies developed throughout the intervention, will be published in the academic literature, presented at national and international conferences, community newsletters, newspapers, infographics and relevant social media.Trial registration numberACTRN12618001986268p.

Introduction Applying systems science in public health trials is a recent innovation in childhood obesity prevention. This paper aims to use systems science conventions to propose a theory of change for community-based interventions aiming to build capacity and use exemplars from systems science for obesity prevention to describe how this approach works. Methods Participants were community-based researchers. A dynamic hypothesis was created in workshops conducted in 2020 and 2021 by identifying variables critical to building community capacity for systems thinking. These were used to develop stock and flow diagrams representing individual causal relationships, feedback loops, and the overall theory of change. Results The resultant model identified 9 stocks and 4 pairs of central balancing and reinforcing feedback loops. These represented building commitment through relationships, mutual learning, strengthening collaboration, and embedding capacity. The model is described using examples from 3 trials involving 25 communities across Victoria, Australia. Conclusions This nonlinear and practice-based model illustrates the process of community-based obesity prevention. The model integrates >20 years of community-based intervention implementation experience, providing an overarching theory of how such interventions work to create change and prevent obesity.

Background To make effective progress towards a global reduction in obesity prevalence, there needs to be a focus on broader structural factors, beyond individual-level drivers of diet and physical activity. This article describes the use of a systems framework to develop obesity prevention policies with adolescents. The aim of this research was to use the group model building (GMB) method to identify young people’s perceptions of the drivers of adolescent obesity in five European countries, as part of the EU-funded Co-Create project. Methods We used GMB with four groups of 16–18-year-olds in schools in each of the five European countries (The Netherlands, Norway, Poland, Portugal and the UK) to create causal loop diagrams (CLDs) representing their perceptions of the drivers of adolescent obesity. The maps were then merged into one, using a new protocol. Results Two hundred and fifty-seven participants, aged 16–18 years, engaged in 20 separate system mapping groups, each of which generated 1 CLD. The findings were largely congruent between the countries. Three feedback loops in the merged diagram particularly stand out: commercial drivers of unhealthy diets; mental health and unhealthy diets; social media use, body image and motivation to exercise. Conclusions GMB provides a novel way of eliciting from young people the system-based drivers of obesity that are relevant to them. Mental health issues, social media use and commercial practices were considered by the young people to be key drivers of adolescent obesity, subjects that have thus far had little or no coverage in research and policy.

Participatory systems thinking methods are often used in community-based participatory research to engage and respond to complexity. Participation in systems thinking activities creates opportunities for participants to gain useful insights about complexity. It is desirable to design activities that extend the benefits of this participation into communities, as these insights are predictive of success in community-based prevention. This study tests an online, computer-mediated participatory system modelling platform (STICKE) and associated methods for collating and analysing its outputs. STICKE was trialled among a group of community members to test a computer-mediated system modelling exercise. The causal diagrams resulting from the exercise were then merged, and network analysis and DEMATEL methods applied to inform the generation of a smaller summary model to communicate insights from the participant group as a whole. Participants successfully completed the online modelling activity, and created causal diagrams consistent with expectations. The DEMATEL analysis was identified as the participant-preferred method for converging individuals causal diagrams into a coherent and useful summary. STICKE is an accessible tool that enabled participants to create causal diagrams online. Methods trialled in this study provide a protocol for combining and summarising individual causal diagrams that was perceived to be useful by the participant group. STICKE supports communities to consider and respond to complex problems at a local level, which is cornerstone of sustainable effective prevention. Understanding how communities perceive their own health challenges will be important to better support and inform locally owned prevention efforts.

Complex health problems require multi-strategy, multi-target interventions. We present a method that uses machine learning techniques to choose optimal interventions from a set of possible interventions within a case study aiming to increase General Practitioner (GP) discussions of physical activity (PA) with their patients. Interventions were developed based on a causal loop diagram with 26 GPs across 13 clinics in Geelong, Australia. GPs prioritised eight from more than 80 potential interventions to increase GP discussion of PA with patients. Following a 2-week baseline, a multi-arm bandit algorithm was used to assign optimal strategies to GP clinics with the target outcome being GP PA discussion rates. The algorithm was updated weekly and the process iterated until the more promising strategies emerged (a duration of seven weeks). The top three performing strategies were continued for 3 weeks to improve the power of the hypothesis test of effectiveness for each strategy compared to baseline. GPs recorded a total of 11,176 conversations about PA. GPs identified 15 factors affecting GP PA discussion rates with patients including GP skills and awareness, fragmentation of care and fear of adverse outcomes. The two most effective strategies were correctly identified within seven weeks of the algorithm-based assignment of strategies. These were clinic reception staff providing PA information to patients at check in and PA screening questionnaires completed in the waiting room. This study demonstrates an efficient way to test and identify optimal strategies from multiple possible solutions.

Summary Introduction The Foresight obesity map represents an expert‐developed systems map describing the complex drivers of obesity. Recently, community‐led causal loop diagrams have been developed to support community‐based obesity prevention interventions. This paper presents a quantitative comparison between the Foresight obesity systems map and a community‐developed map of the drivers of obesity. Methods Variables from a community‐developed map were coded against the thematic clusters defined in the Foresight map to allow comparison of their sizes and strength of adjoining causal relationships. Central variables were identified using techniques from network analysis. These properties were compared to understand the similarities and differences between the systems as defined by the two groups. Results The community map focused on environmental influences, such as built physical activity environment (18% of variables) and social psychology (38%). The Foresight map's largest cluster was physiology (23%), a minimal focus in the community map (2%). Network analysis highlighted media and available time within both maps, but variables related to school and sporting club environments were unique to the community map. Conclusion Community stakeholders focus on modifiable social and environmental drivers of obesity. Capturing local perspectives is critical when using systems maps to guide community‐based obesity prevention.

Introduction Health related quality of life is a multi-dimensional construct of particular interest in determining the consequences of illness and disease. This study aimed to determine the relationships between overweight/obesity, and associated obesogenic risk behaviours with health related quality of life and physical, social, emotional and school sub-domains, among a large cohort of Australian primary school children. Methods The data were derived from the Goulburn Valley Health Behaviours Monitoring study whereby a census-styled school recruitment process and high participatory opt-out (passive) procedure was employed. All primary schools in three Local Government Areas were invited to participate between July-September 2016 with 39/62 (62%) of schools participating and 1606/2034 (79%) students in Grade 2 (aged approx. 7-8 years), Grade 4 (aged approx. 9-10 years) and Grade 6 (aged approx. 11-12 years) participating. Measured height and weight were collected among participating students and older children (Grade 4 and 6) who also completed a self-report behavioural questionnaire, including the paediatric quality of life inventory. Results Among 809 children aged 9 to 12 years, there were 219 (27.1%) classified as overweight/obese. Male children classified as overweight/obese reported significantly lower health related quality of life in the physical functioning and global functioning scores, compared to normal weight males. Significantly higher quality of life scores were observed among all children who met the physical activity recommendations on five out of the seven previous days. Significantly higher scores were observed among males adhering to the daily screen time recommendations, and among those meeting daily recommendations for fruit consumption. Among male school children, soft drink consumption was associated to lower health related quality of life. Conclusion Although cross-sectional, these findings highlight children with overweight/obesity and some underlying lifestyle behavioural risk factors, had significantly lower healthy-related quality of life, although this was observed most consistently among male school children. These findings have not previously been identified in young children and highlights the need to consider mental and emotional health in public health efforts to prevent obesity. Trial Registration ANZCTR Trial Registry: ACTRN12616000980437 retrospectively registered 26 July 2016 Electronic supplementary material The online version of this article (10.1186/s12955-019-1086-0) contains supplementary material, which is available to authorized users.

Objective: Depression affects many Australian adolescents. Research points to the potential of lifestyle improvement for the population-level prevention of mental disorders. However, most studies examine single relationships without considering the combined contribution of lifestyle factors to variance in depression. This study examined associations between adolescent diet, physical activity and screen time behaviours and depressive symptomatology. Methods: A cross-sectional sample of year 8 and 10 students was recruited from 23 participating schools in 18 Victorian communities. Students were recruited using opt-out consent, resulting in 3295 participants from 4680 registered school enrolments (Participation Rate: 70.4%). Participants completed a supervised self-report questionnaire comprising Moods and Feelings Questionnaire–Short Form, an assessment of physical activity and sedentary behaviours during and outside school, and weekly food intake. Surveyed covariates included hours of sleep per night, age, socio-economic status and measured anthropometry. A hierarchical regression stratified by gender was conducted, with dichotomised Moods and Feelings Questionnaire–Short Form score as the outcome, and screen time, physical activity and dietary patterns as predictors. Nested regression analyses were then conducted to ascertain the variance in Moods and Feelings Questionnaire–Short Form score attributable to each significant predictor from the initial regression. Results: Increased scores on an unhealthy dietary pattern (odds ratio = 1.18; 95% confidence interval = [1.07, 1.32]) and physical activity guideline attainment (0.91; [0.85, 0.97]) were associated with depressive symptomatology in males, while screen time guideline attainment (0.95; [0.91, 0.98]) was associated with depression in females. No association was observed between healthy diet pattern and Moods and Feelings Questionnaire–Short Form. Overall, effect sizes were generally small, and the regression model accounted for 5.22% of Moods and Feelings Questionnaire–Short Form variance. Conclusion: Gender-specific associations were observed between physical activity and both sedentary and dietary behaviours and depressive symptomatology among adolescents, although reverse causality cannot be refuted at this stage. Lifestyle behaviours may represent a modifiable target for the prevention of depressive symptomatology in adolescents.

High school completion, like many educational phenomena, is the result of processes that, when taken together, constitute a complex system. In this paper, we describe the innovative use of group model building (GMB) as an entry point for complex systems analysis of educational processes that collectively determine high school completion. GMB exercises were conducted in a community in the state of Victoria in Australia. GMB brought together stakeholders from around the community and encouraged them to view high school completion from a complex systems perspective. Not only were participants able to use their experience to create an action plan to increase high school graduation rates but they also provided researchers with valuable information that can be used as input into rigorous, quantitative models of high school completion. Copyright © 2016 John Wiley & Sons, Ltd.

BACKGROUND/OBJECTIVES: Body weight is negatively associated with adolescent Health-Related Quality of Life (HRQoL). Despite this well-established relationship, some adolescents with obesity do not display the expected HRQoL decreases. This study hypothesised weight perception as a moderator of the association between weight status and adolescent HRQoL. SUBJECTS/METHODS: Subjects were secondary school students from an obesity prevention project in the Barwon South-West region of Victoria, Australia, entitled It's Your Move (N = 3040). Measures included standardised body mass index (BMI-z; World Health Organization growth standards), weight perception and HRQoL, measured by the Paediatric Quality of Life Inventory. Linear regression and average marginal effect analyses were conducted on cross-sectional baseline data to determine the significance of any interaction between weight perception and measured weight status in shaping adolescent HRQoL. RESULTS: The BMI-z/perceived weight status interaction was significantly associated with adolescent HRQoL outcomes. Adolescents with BMI z-scores in the overweight/obesity range who perceived themselves as overweight had lower HRQoL than those who perceived themselves as 'about right.' Conversely, adolescents with BMI scores in the lower end of the normal range or in the thinness range who perceived themselves as underweight had lower HRQoL than those with 'about right' perceptions. CONCLUSIONS: This was the first study to report third-variable impacts of a body-perception variable on the relationship between adolescent weight status and HRQoL. Adolescents' weight perceptions significantly moderated the relationship between overweight/obesity and reduced HRQoL. Adolescents who were outside the normal weight range and misperceived their objectively measured weight status enjoyed a higher HRQoL than adolescents whose weight perception was concordant with their actual weight status. These findings suggest that practitioners may need to exercise caution when educating adolescents about their weight status, as such 'reality checks' may negatively impact on adolescent HRQoL. It is suggested that more research be conducted to examine this potential effect.

Background: Tracking population trends in childhood obesity and identifying target areas for prevention requires accurate prevalence data. This study quantified the magnitude of non-participation bias for mean Body Mass Index-z scores and overweight/obesity prevalence associated with low (opt-in) compared to high (opt-out) participation consent methodologies. Methods: Data arose from all Local Government Areas (LGAs) participating in the Healthy Together Victoria Childhood Obesity Study, Australia. Primary schools were randomly selected in 2013 and 2014 and all Grades 4 and 6 students (aged approx. 9–12 years) were invited to participate via opt-in consent (2013) and opt-out consent (2014). For the opt-in wave N = 38 schools (recruitment rate (RR) 24.3%) and N = 856 students participated (RR 36.3%). For the opt-out wave N = 47 schools (RR 32%) and N = 2557 students participated (RR 86.4%). Outcomes: differences between opt-in and opt-out sample estimates (bias) for mean BMI-z, prevalence of overweight/obesity and obesity (alone). Standardized bias (Std bias) estimates defined as bias/standard error are reported for BMI-z. Results: The results demonstrate strong evidence of non-participation bias for mean BMI-z overall (Std bias = −4.5, p < 0.0001) and for girls (Std bias = −5.4, p < 0.0001), but not for boys (Std bias = −1.1, p = 0.15). The opt-in strategy underestimated the overall population prevalence of overweight/obesity and obesity by −5.4 and −4.5 percentage points respectively (p < 0.001 for both). Significant underestimation was seen in girls, but not for boys. Conclusions: Opt-in consent underestimated prevalence of childhood obesity, particularly in girls. Prevalence, monitoring and community intervention studies on childhood obesity should move to opt-out consent processes for better scientific outcomes.

Background: Emerging evidence supports a relationship between risk factors for obesity and the genesis of the common mental disorders, depression and anxiety. This suggests common mental disorders should be considered as a form of non-communicable disease, preventable through the modification of lifestyle behaviours, particularly diet and physical activity. Discussion: Obesity prevention research since the 1970’s represents a considerable body of knowledge regarding strategies to modify diet and physical activity and so there may be clear lessons from obesity prevention that apply to the prevention of mental disorders. For obesity, as for common mental disorders, adolescence represents a key period of vulnerability. In this paper we briefly discuss relationships between modifiable lifestyle risk factors and mental health, lifestyle risk factor interventions in obesity prevention research, the current state of mental health prevention, and the implications of current applications of systems thinking in obesity prevention research for lifestyle interventions. Summary: We propose a potential focus for future mental health promotion interventions and emphasise the importance of lessons available from other lifestyle modification intervention programmes.