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Leveraging social influence to address overweight and obesity using agent-based models: The role of adolescent social networks

Overview of attention for article published in Social Science & Medicine, June 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

news
5 news outlets
blogs
1 blog
policy
1 policy source
twitter
7 X users
wikipedia
1 Wikipedia page

Citations

dimensions_citation
77 Dimensions

Readers on

mendeley
221 Mendeley
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Title
Leveraging social influence to address overweight and obesity using agent-based models: The role of adolescent social networks
Published in
Social Science & Medicine, June 2014
DOI 10.1016/j.socscimed.2014.05.049
Pubmed ID
Authors

J. Zhang, L. Tong, P.J. Lamberson, R.A. Durazo-Arvizu, A. Luke, D.A. Shoham

Abstract

The prevalence of adolescent overweight and obesity (hereafter, simply "overweight") in the US has increased over the past several decades. Individually-targeted prevention and treatment strategies targeting individuals have been disappointing, leading some to propose leveraging social networks to improve interventions. We hypothesized that social network dynamics (social marginalization; homophily on body mass index, BMI) and the strength of peer influence would increase or decrease the proportion of network member (agents) becoming overweight over a simulated year, and that peer influence would operate differently in social networks with greater overweight. We built an agent-based model (ABM) using results from R-SIENA. ABMs allow for the exploration of potential interventions using simulated agents. Initial model specifications were drawn from Wave 1 of the National Longitudinal Study of Adolescent Health (Add Health). We focused on a single saturation school with complete network and BMI data over two waves (n = 624). The model was validated against empirical observations at Wave 2. We focused on overall overweight prevalence after a simulated year. Five experiments were conducted: (1) changing attractiveness of high-BMI agents; (2) changing homophily on BMI; (3) changing the strength of peer influence; (4) shifting the overall BMI distribution; and (5) targeting dietary interventions to highly connected individuals. Increasing peer influence showed a dramatic decrease in the prevalence of overweight; making peer influence negative (i.e., doing the opposite of friends) increased overweight. However, the effect of peer influence varied based on the underlying distribution of BMI; when BMI was increased overall, stronger peer influence increased proportion of overweight. Other interventions, including targeted dieting, had little impact. Peer influence may be a viable target in overweight interventions, but the distribution of body size in the population needs to be taken into account. In low-obesity populations, strengthening peer influence may be a useful strategy.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 221 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
Bahrain 1 <1%
Tunisia 1 <1%
Belgium 1 <1%
Unknown 216 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 49 22%
Student > Master 40 18%
Student > Doctoral Student 18 8%
Researcher 16 7%
Professor > Associate Professor 14 6%
Other 44 20%
Unknown 40 18%
Readers by discipline Count As %
Social Sciences 43 19%
Medicine and Dentistry 32 14%
Psychology 17 8%
Nursing and Health Professions 14 6%
Computer Science 12 5%
Other 51 23%
Unknown 52 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 46. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 December 2023.
All research outputs
#902,163
of 25,373,627 outputs
Outputs from Social Science & Medicine
#871
of 11,875 outputs
Outputs of similar age
#8,524
of 241,454 outputs
Outputs of similar age from Social Science & Medicine
#17
of 153 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,875 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.8. This one has done particularly well, scoring higher than 92% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 241,454 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 153 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.