Poster Presentation ANZOS-OSSANZ-AOCO Joint Annual Scientific Meeting 2017

Community obesity prevention networks at baseline: a social network perspective (#267)

Jennifer Marks 1 , Andrew Sanigorski 1 , Brynle Owen , Jaimie McGlashan 1 , Lynne Millar , Melanie Nichols 1 , Claudia Strugnell 1 , Steve Allender 1
  1. Global Obesity Centre, Deakin University, Geelong, Vic, Australia


Community-based obesity prevention efforts are dependent on the strength and function of collaborative networks across multiple community systems. There is little empirical work on understanding how community network structure influences obesity prevention capacity. We describe network structures within nineteen local government communities prior to a large-scale community based obesity prevention intervention, Healthy Together Victoria, Australia (2012-2015).


Participants were from a large, multi-site, cluster randomized trial (cRCT) of a whole of systems chronic disease prevention initiative. Community leaders from twelve prevention and seven comparison regions identified and described their professional networks in relation to dietary, physical activity and weight gain issues in young children (<5 years of age) within their community. Social network measures of density, modularity, clustering and centrality were calculated for each community. Comparison of means and tests of association were conducted for each network relationship.


One-hundred and seven respondents (78 intervention; 29 comparison) reported on 1,000 professional network relationships (respondent average: 10 intervention; 8 comparison). Networks were typically sparse, highly modular, heterogeneous in size and relationship composition. Frequency of interaction, close and influential relationships were inversely associated with network density.

Discussion and Conclusion: At baseline in this cRCT there were no significant differences in network structures related to key actors with remit to influence environments affecting dietary, physical activity and weight gain decisions of children. Tracking heterogeneity in both networks and measured outcomes over time may help explain the interaction between these two vital aspects of community based intervention.