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Applications of social network analysis to obesity: a systematic review
Author(s) -
Zhang S.,
Haye K.,
Ji M.,
An R.
Publication year - 2018
Publication title -
obesity reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.845
H-Index - 162
eISSN - 1467-789X
pISSN - 1467-7881
DOI - 10.1111/obr.12684
Subject(s) - exponential random graph models , popularity , social network (sociolinguistics) , friendship , social network analysis , degree distribution , psychology , odds , psychological intervention , obesity , modularity (biology) , social capital , complex network , social psychology , computer science , random graph , graph , medicine , sociology , social media , world wide web , biology , machine learning , social science , logistic regression , psychiatry , genetics , theoretical computer science
Summary People's health behaviours and outcomes can be profoundly shaped by the social networks they are embedded in. Based on graph theory, social network analysis is a research framework for the study of social interactions and the structure of these interactions among social actors. A literature search was conducted in PubMed and Web of Science for articles published until August 2017 that applied social network analysis to examine obesity and social networks. Eight studies (three cross‐sectional and five longitudinal) conducted in the US ( n = 6) and Australia ( n = 2) were identified. Seven focused on adolescents' and one on adults' friendship networks. They examined structural features of these networks that were associated with obesity, including degree distribution, popularity, modularity maximization and K‐clique percolation. All three cross‐sectional studies that used exponential random graph models found individuals with similar body weight status and/or weight‐related behaviour were more likely to share a network tie than individuals with dissimilar traits. Three longitudinal studies using stochastic actor‐based models found friendship network characteristics influenced change in individuals' body weight status and/or weight‐related behaviour over time. Future research should focus on diverse populations and types of social networks and identifying the mechanisms by which social networks influence obesity to inform network‐based interventions.