Networks for prevention in 19 communities at the start of a large-scale community-based obesity prevention initiative

Abstract

Community-based obesity prevention efforts are dependent on the strength and function of collaborative networks across multiple community members and organizations. There is little empirical work on understanding how community network structure influences obesity prevention capacity. We describe network structures within 19 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 12 intervention and seven comparison (non-intervention) regions identified and described their professional networks in relation to dietary, physical activity, and weight status among young children (<5 years of age). 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 996 professional network relationships (respondent average per region: 10 intervention; 8 comparison). Networks were typically sparse and highly modular. Networks were heterogeneous in size and relationship composition. Frequency of interaction, close and influential relationships were inversely associated with network density. At baseline in this cRCT there were no significant differences between community network structures of key actors with influence over environments affecting children's diet and physical activity. Tracking heterogeneity in both networks and measured outcomes over time may help explain the interaction between professional networks and intervention effectiveness of community-based obesity prevention.

Social Network Structures in African American Churches: Implications for Health Promotion Programs

“…If density is too low, intervention strategies may be needed to build connections within the intervention groups by including networking activities [38]. Along the same lines, in the recent study by Marks et al, they described the network structure of 19 community organizations prior to implementation of a community-based obesity prevention intervention [39]. Contrary to their SE standard error, AinS alternating k-instar, AoutS alternating k-outstar, AT-T alternating k-triangle effect, A2P-T alternating 2-path effect Note: Network A (=Church A), SD standard deviation Density: the number of ties present divided by number of possible ties.…”

“…Component: Set of individuals who are connected with another, but not with others in the community (all nonisolates). Isolates: People not connected to anyone hypothesis, they found that networks were spare (low density) and not well connected, which may have negatively influenced the success of community level efforts to prevent obesity [39]. They emphasized that in planning of community-based obesity interventions, we cannot assume the pre-existence of strong networks and deliberate fostering to increase connections is required to improve information flow and group cohesion [39].…”

“…Isolates: People not connected to anyone hypothesis, they found that networks were spare (low density) and not well connected, which may have negatively influenced the success of community level efforts to prevent obesity [39]. They emphasized that in planning of community-based obesity interventions, we cannot assume the pre-existence of strong networks and deliberate fostering to increase connections is required to improve information flow and group cohesion [39]. Centralization in the church networks was low which met recommended threshold for group health intervention (< 0.25) [38].…”

“…In summary, the application of social network analysis to group-and community level-health interventions is an emerging field and many opportunities remain open [22,39,44,45]. Understanding social network structure based on sociocentric network analysis in African American churches may be important to develop health promotion programs and improve the effect of the interventions by allocating resources efficiently and targeting health problems and behaviors.…”

Collaboration in Complex Systems: Multilevel Network Analysis for Community-Based Obesity Prevention Interventions

“…Innovation in the field includes the application of social network analysis (SNA) to measure and quantitatively analyse the patterns of relationships (i.e., networks) among steering committee members, as a tool for intervention evaluation 15–17 . In a substance abuse prevention initiative that leveraged community coalitions, Valente et al .…”