Evidence map›Paper›PMID 30016518›Full record

Trial reportTranslational behavioral medicine2018

Networks for prevention in 19 communities at the start of a large-scale community-based obesity prevention initiative.

Jennifer Marks, Andrew Sanigorski, Brynle Owen, Jaimie McGlashan, Lynne Millar, Melanie Nichols, Claudia Strugnell, Steven Allender

Abstract readMulticenter StudyRandomized Controlled Trial
In one paragraph

Trial report in Translational behavioral medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Interventions to prevent obesity in children aged 2 to 4 years old.The Cochrane database of systematic reviews · 2025
    Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Social Network Structures in African American Churches: Implications for Health Promotion Programs.Journal of urban health : bulletin of the New York Academy of Medicine · 2019
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jennifer MarksDeakin University, Geelong Australia, Global Obesity Centre, Centre for Population Health Research.
Andrew SanigorskiDeakin University, Geelong Australia, Global Obesity Centre, Centre for Population Health Research.
Brynle OwenDeakin University, Geelong Australia, Global Obesity Centre, Centre for Population Health Research.
Jaimie McGlashanDeakin University, Geelong Australia, Global Obesity Centre, Centre for Population Health Research.
Lynne MillarAustralian Health Policy Collaboration, Victoria University, Melbourne, Australia.
Melanie NicholsDeakin University, Geelong Australia, Global Obesity Centre, Centre for Population Health Research.
Claudia StrugnellDeakin University, Geelong Australia, Global Obesity Centre, Centre for Population Health Research.
Steven AllenderDeakin University, Geelong Australia, Global Obesity Centre, Centre for Population Health Research.

Funding

Systems Science to Guide Whole-of-Community Childhood Obesity InterventionsR01HL115485 · NHLBI · HARVARD PILGRIM HEALTH CARE, INC. · PI ECONOMOS, CHRISTINA D, HAMMOND, ROSS A. · 2013 to 2017
$3.4M
Big data apprOaches fOr Safe Therapeutics in Healthy Pregnancies (BOOST-HP)R01HD110107 · NICHD · HARVARD PILGRIM HEALTH CARE, INC. · PI MARO, JUDITH, WINTERSTEIN, ALMUT G · 2022 to 2025
$2.5M
NHLBI NIH HHS R01 HL115485NICHD NIH HHS R01 HD110107
6 · The paper itself

Abstract

Community-based obesity prevention efforts are dependent on the strength and function of collaborative networks across multiple community members and organizations. There is little empirical work on understanding how community network structure influences obesity prevention capacity. We describe network structures within 19 local government communities prior to a large-scale community-based obesity prevention intervention, Healthy Together Victoria, Australia (2012-2015). Participants were from a large, multi-site, cluster randomized trial (cRCT) of a whole-of-systems chronic disease prevention initiative. Community leaders from 12 intervention and seven comparison (non-intervention) regions identified and described their professional networks in relation to dietary, physical activity, and weight status among young children (<5 years of age). Social network measures of density, modularity, clustering, and centrality were calculated for each community. Comparison of means and tests of association were conducted for each network relationship. One-hundred and seven respondents (78 intervention; 29 comparison) reported on 996 professional network relationships (respondent average per region: 10 intervention; 8 comparison). Networks were typically sparse and highly modular. Networks were heterogeneous in size and relationship composition. Frequency of interaction, close and influential relationships were inversely associated with network density. At baseline in this cRCT there were no significant differences between community network structures of key actors with influence over environments affecting children's diet and physical activity. Tracking heterogeneity in both networks and measured outcomes over time may help explain the interaction between professional networks and intervention effectiveness of community-based obesity prevention.

Indexed as

Community NetworksAdultFemaleHealth PromotionHumansInterpersonal RelationsMaleMiddle AgedObesityRetrospective StudiesSocial Networking

Identifiers

PMID30016518
PMCPMC6457086

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.