IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0220169.html
   My bibliography  Save this article

Design and methods of Shape Up Under 5: Integration of systems science and community-engaged research to prevent early childhood obesity

Author

Listed:
  • Julia M Appel
  • Karen Fullerton
  • Erin Hennessy
  • Ariella R Korn
  • Alison Tovar
  • Steven Allender
  • Peter S Hovmand
  • Matt Kasman
  • Boyd A Swinburn
  • Ross A Hammond
  • Christina D Economos

Abstract

Shape Up Under 5 (SUU5) was a two-year early childhood obesity prevention pilot study in Somerville, Massachusetts (2015–2017) designed to test a novel conceptual framework called Stakeholder-driven Community Diffusion. For whole-of-community interventions, this framework posits that diffusion of stakeholders’ knowledge about and engagement with childhood obesity prevention efforts through their social networks will improve the implementation of health-promoting policy and practice changes intended to reduce obesity risk. SUU5 used systems science methods (agent-based modeling, group model building, social network analysis) to design, facilitate, and evaluate the work of 16 multisector stakeholders (‘the Committee’). In this paper, we describe the design and methods of SUU5 using the conceptual framework: the approach to data collection, and methods and rationale for study inputs, activities and evaluation, which together may further our understanding of the hypothesized processes within Stakeholder-driven Community Diffusion. We also present a generalizable conceptual framework for addressing childhood obesity and similar complex public health issues through whole-of-community interventions.

Suggested Citation

  • Julia M Appel & Karen Fullerton & Erin Hennessy & Ariella R Korn & Alison Tovar & Steven Allender & Peter S Hovmand & Matt Kasman & Boyd A Swinburn & Ross A Hammond & Christina D Economos, 2019. "Design and methods of Shape Up Under 5: Integration of systems science and community-engaged research to prevent early childhood obesity," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-10, August.
  • Handle: RePEc:plo:pone00:0220169
    DOI: 10.1371/journal.pone.0220169
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0220169
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0220169&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0220169?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Thompson, Amanda L. & Bentley, Margaret E., 2013. "The critical period of infant feeding for the development of early disparities in obesity," Social Science & Medicine, Elsevier, vol. 97(C), pages 288-296.
    2. Cowan, Robin & Jonard, Nicolas, 2004. "Network structure and the diffusion of knowledge," Journal of Economic Dynamics and Control, Elsevier, vol. 28(8), pages 1557-1575, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sisitha Jayasinghe & Robert Soward & Lisa Dalton & Timothy P. Holloway & Sandra Murray & Kira A. E. Patterson & Kiran D. K. Ahuja & Roger Hughes & Nuala M. Byrne & Andrew P. Hills, 2022. "Domains of Capacity Building in Whole-Systems Approaches to Prevent Obesity—A “Systematized” Review," IJERPH, MDPI, vol. 19(17), pages 1-17, September.
    2. Berezvai, Zombor & Vitrai, József & Tóth, Gergely & Brys, Zoltán & Bakacs, Márta & Joó, Tamás, 2024. "Long-term impact of unhealthy food tax on consumption and the drivers behind: A longitudinal study in Hungary," Health Policy, Elsevier, vol. 146(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stephen Chen, 2009. "Corporate Responsibilities in Internet-Enabled Social Networks," Journal of Business Ethics, Springer, vol. 90(4), pages 523-536, December.
    2. Anil K. Gupta & Paul E. Tesluk & M. Susan Taylor, 2007. "Innovation At and Across Multiple Levels of Analysis," Organization Science, INFORMS, vol. 18(6), pages 885-897, December.
    3. Li, Ziran & Sun, Jiajing & Wang, Shouyang, 2013. "Amplitude-Duration-Persistence Trade-off Relationship for Long Term Bear Stock Markets," MPRA Paper 54177, University Library of Munich, Germany.
    4. Daniele Cassese & Paolo Pin, 2018. "Decentralized Pure Exchange Processes on Networks," Papers 1803.08836, arXiv.org, revised Mar 2022.
    5. Liming Zhao & Haihong Zhang & Wenqing Wu, 2019. "Cooperative knowledge creation in an uncertain network environment based on a dynamic knowledge supernetwork," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 657-685, May.
    6. Rosina Moreno & Ernest Miguélez, 2012. "A Relational Approach To The Geography Of Innovation: A Typology Of Regions," Journal of Economic Surveys, Wiley Blackwell, vol. 26(3), pages 492-516, July.
    7. Mueller, Matthias & Bogner, Kristina & Buchmann, Tobias & Kudic, Muhamed, 2015. "Simulating knowledge diffusion in four structurally distinct networks: An agent-based simulation model," Hohenheim Discussion Papers in Business, Economics and Social Sciences 05-2015, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    8. Zhu, Hongmiao & Jin, Zhen & Yan, Xin, 2023. "A dynamics model of coupling transmission for multiple different knowledge in multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(C).
    9. Wang, Haiying & Moore, Jack Murdoch & Wang, Jun & Small, Michael, 2021. "The distinct roles of initial transmission and retransmission in the persistence of knowledge in complex networks," Applied Mathematics and Computation, Elsevier, vol. 392(C).
    10. Mario V. Tomasello & Mauro Napoletano & Antonios Garas & Frank Schweitzer, 2017. "The rise and fall of R&D networks," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 26(4), pages 617-646.
    11. Cantner, Uwe & Graf, Holger, 2006. "The network of innovators in Jena: An application of social network analysis," Research Policy, Elsevier, vol. 35(4), pages 463-480, May.
    12. Dimitris Tsintsaris & Milan Tsompanoglou & Evangelos Ioannidis, 2024. "Dynamics of Social Influence and Knowledge in Networks: Sociophysics Models and Applications in Social Trading, Behavioral Finance and Business," Mathematics, MDPI, vol. 12(8), pages 1-27, April.
    13. Roth, Camille, 2007. "Empiricism for descriptive social network models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(1), pages 53-58.
    14. Stefano Usai & Emanuela Marrocu & Raffaele Paci, 2017. "Networks, Proximities, and Interfirm Knowledge Exchanges," International Regional Science Review, , vol. 40(4), pages 377-404, July.
    15. Nicola Lacetera, 2003. "Incentives and spillovers in R&D activities: an agency-theoretic analysis of industry-university relations," Microeconomics 0312004, University Library of Munich, Germany.
    16. Pier Patrucco, 2008. "The economics of collective knowledge and technological communication," The Journal of Technology Transfer, Springer, vol. 33(6), pages 579-599, December.
    17. Menger Tu & Sandy Dall'erba & Mingque Ye, 2022. "Spatial and Temporal Evolution of the Chinese Artificial Intelligence Innovation Network," Sustainability, MDPI, vol. 14(9), pages 1-17, April.
    18. Cremonini, Marco, 2016. "Introducing serendipity in a social network model of knowledge diffusion," Chaos, Solitons & Fractals, Elsevier, vol. 90(C), pages 64-71.
    19. Mauro Napoletano & Stefano Battiston & Michael D König & Frank Schweitzer, 2008. "The efficiency and evolution of R&D Networks," Working Papers hal-01066189, HAL.
    20. Giuseppe Calignano & Rune Dahl Fitjar, 2017. "Strengthening relationships in clusters: How effective is an indirect policy measure carried out in a peripheral technology district?," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 59(1), pages 139-169, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0220169. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.