About this Journal Submit a Manuscript Table of Contents
Journal of Obesity
Volume 2013 (2013), Article ID 919287, 9 pages
http://dx.doi.org/10.1155/2013/919287
Research Article

Using Social Network Analysis to Identify Key Child Care Center Staff for Obesity Prevention Interventions: A Pilot Study

1WHO Collaborating Center for Obesity Prevention, Deakin University, Geelong, VIC 3220, Australia
2School of Health and Social Development, Deakin University, Burwood, VIC 3125, Australia
3Healthy Together Geelong, City of Greater Geelong, Geelong, VIC 3220, Australia
4Population Health and Inequalities Research Center University of Calgary, Calgary, Canada T2N 4N1

Received 26 March 2013; Revised 26 June 2013; Accepted 9 July 2013

Academic Editor: Sabina Gesell

Copyright © 2013 Jennifer Marks et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Introduction. Interest has grown in how systems thinking could be used in obesity prevention. Relationships between key actors, represented by social networks, are an important focus for considering intervention in systems. Method. Two long day care centers were selected in which previous obesity prevention programs had been implemented. Measures showed ways in which physical activity and dietary policy are conversations and actions transacted through social networks (interrelationships) within centers, via an eight item closed-ended social network questionnaire. Questionnaire data were collected from (17/20; response rate 85%) long day care center staff. Social network density and centrality statistics were calculated, using UCINET social network software, to examine the role of networks in obesity prevention. Results. “Degree” (influence) and “betweeness” (gatekeeper) centrality measures of staff inter-relationships about physical activity, dietary, and policy information identified key players in each center. Network density was similar and high on some relationship networks in both centers but markedly different in others, suggesting that the network tool identified unique center social dynamics. These differences could potentially be the focus of future team capacity building. Conclusion. Social network analysis is a feasible and useful method to identify existing obesity prevention networks and key personnel in long day care centers.