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Updated: Jun 5, 2026

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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The 'GROW Social Network' datasets.

Sabina B Gesell1, Evan C Sommer2, Shari L Barkin2

  • 1Department of Social Sciences and Health Policy, Department of Implementation Science, Wake Forest School of Medicine, Winston-Salem, North Carolina.

Connections (Toronto, Ont.)
|June 4, 2026
PubMed
Summary
This summary is machine-generated.

This study examined social network data from a pediatric obesity prevention program. Findings highlight the structure of social connections and perceived cohesion within families and intervention groups.

Keywords:
Healthy lifestyleHispanicRandomized controlled trialSocial networks

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Published on: November 4, 2025

Area of Science:

  • Public Health
  • Social Network Analysis
  • Pediatric Obesity

Background:

  • Pediatric obesity is a significant public health concern requiring effective prevention strategies.
  • Community-based family interventions show promise in addressing childhood obesity.
  • Understanding social dynamics within interventions is crucial for optimizing engagement and outcomes.

Purpose of the Study:

  • To compile and describe datasets from the GROW Social Network study, a 3-year pediatric obesity prevention intervention.
  • To characterize multiplex social network structures among adult participants over time.
  • To analyze social cohesion within intervention subgroups.

Main Methods:

  • The GROW Social Network dataset includes multiplex network data from 610 adult participants across four timepoints.
  • Data were collected on social connections (edges) between participants and within small intervention subgroups.
  • Perceived cohesion was measured using a validated self-report instrument at multiple timepoints.

Main Results:

  • The datasets provide a rich characterization of social network structures and actor attributes.
  • Analysis revealed multiplex social connections at individual and group levels.
  • Temporal data capture changes in network structure and cohesion throughout the intervention.

Conclusions:

  • The GROW Social Network datasets offer valuable resources for studying social influences on pediatric obesity prevention.
  • These data enable detailed examination of network dynamics and their relationship to intervention success.
  • Further research can leverage these datasets to develop targeted social network-based interventions.