An Actor-Based Model of Social Network Influence on Adolescent Body Size, Screen Time, and Playing Sports

David A Shoham,Jay S Kaufman,Jun Zhang,Richard S Cooper,Amy Luke,Liping Tong,Peter J Lamberson,Amy H Auchincloss,Lara Dugas,Kathleen A O’Connor

doi:10.1371/journal.pone.0039795

David A Shoham, Jay S Kaufman + Show 8 more

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https://doi.org/10.1371/journal.pone.0039795

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Abstract

Recent studies suggest that obesity may be “contagious” between individuals in social networks. Social contagion (influence), however, may not be identifiable using traditional statistical approaches because they cannot distinguish contagion from homophily (the propensity for individuals to select friends who are similar to themselves) or from shared environmental influences. In this paper, we apply the stochastic actor-based model (SABM) framework developed by Snijders and colleagues to data on adolescent body mass index (BMI), screen time, and playing active sports. Our primary hypothesis was that social influences on adolescent body size and related behaviors are independent of friend selection. Employing the SABM, we simultaneously modeled network dynamics (friendship selection based on homophily and structural characteristics of the network) and social influence. We focused on the 2 largest schools in the National Longitudinal Study of Adolescent Health (Add Health) and held the school environment constant by examining the 2 school networks separately (N = 624 and 1151). Results show support in both schools for homophily on BMI, but also for social influence on BMI. There was no evidence of homophily on screen time in either school, while only one of the schools showed homophily on playing active sports. There was, however, evidence of social influence on screen time in one of the schools, and playing active sports in both schools. These results suggest that both homophily and social influence are important in understanding patterns of adolescent obesity. Intervention efforts should take into consideration peers’ influence on one another, rather than treating “high risk” adolescents in isolation.

Highlights

Childhood obesity is epidemic in the U.S [1,2]
Descriptive Statistics The characteristics of students in the two schools are listed in Number of respondents Age Range of grades Male Non-Hispanic Black Hispanic Household Income ($1 k) Mean body mass index (BMI) Mean BMI Range of BMI Screen time (h/wk) Active sport score Total number of ties Out-degree Reciprocated ties Transitive triplets Sum of BMI similarities1 BMI avg. similarity2 Sum of screen time similarities Screen time avg. similarity Sum of active sport similarities Active sport avg. similarity
The estimates may be likened to the weight that each individual places upon network and alter attributes in deciding to add or drop a Jefferson High Sunshine High basic rate parameter friendship2 1: outdegree3 2: reciprocity4 3: transitive triplets5 4: same sex6 5: same grade6 6: same black race6 7: same Hispanic ethnicity6 8: age similarity6 9: income similarity6

Summary

Introduction

Childhood obesity is epidemic in the U.S [1,2]. Recent data show that that 18.1% of adolescents (ages 12–19 years old) are obese (defined as exceeding the historical 95th percentile of ageand sex-specific body mass index (BMI)) [3]. A 2011 Cochrane Review by Waters and colleagues showed that interventions aimed at reducing obesity in 13-to-18 year old adolescents lowered BMI by an average of 0.09 kg/m2 [5]. The failure of these interventions, especially those targeting individuals, has spurred researchers to identify social and economic influences and suggest novel population-level interventions [6]. Along these lines, recent studies support an etiologic role for social networks in the production and maintenance of childhood and adult obesity [7,8,9,10]

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