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Identifying Social Network Conditions that Facilitate Sedentary Behavior Change: The Benefit of Being a “Bridge” in a Group-based Intervention

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  • Sabina B. Gesell

    (Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
    Department of Implementation Science, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA)

  • Kayla de la Haye

    (Department of Preventive Medicine, University of Southern California, Los Angeles, CA 90007, USA)

  • Evan C. Sommer

    (Department of Pediatrics, Division of Academic General Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37232, USA)

  • Santiago J. Saldana

    (Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA)

  • Shari L. Barkin

    (Department of Pediatrics, Division of Academic General Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37232, USA)

  • Edward H. Ip

    (Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA)

Abstract

Using data from one of the first trials to try to leverage social networks as a mechanism for obesity intervention, we examined which social network conditions amplified behavior change. Data were collected as part of a community-based healthy lifestyle intervention in Nashville, USA, between June 2014 and July 2017. Adults randomized to the intervention arm were assigned to a small group of 10 participants that met in person for 12 weekly sessions. Intervention small group social networks were measured three times; sedentary behavior was measured by accelerometry at baseline and 12 months. Multivariate hidden Markov models classified people into distinct social network trajectories over time, based on the structure of the emergent network and where the individual was embedded. A multilevel regression analysis assessed the relationship between network trajectory and sedentary behavior (N = 261). Being a person that connected clusters of intervention participants at any point during the intervention predicted an average reduction of 31.3 min/day of sedentary behavior at 12 months, versus being isolated [95% CI: (−61.4, −1.07), p = 0.04]. Certain social network conditions may make it easier to reduce adult sedentary behavior in group-based interventions. While further research will be necessary to establish causality, the implications for intervention design are discussed.

Suggested Citation

  • Sabina B. Gesell & Kayla de la Haye & Evan C. Sommer & Santiago J. Saldana & Shari L. Barkin & Edward H. Ip, 2020. "Identifying Social Network Conditions that Facilitate Sedentary Behavior Change: The Benefit of Being a “Bridge” in a Group-based Intervention," IJERPH, MDPI, vol. 17(12), pages 1-13, June.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:12:p:4197-:d:370641
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    References listed on IDEAS

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    1. Ruth F Hunter & Kayla de la Haye & Jennifer M Murray & Jennifer Badham & Thomas W Valente & Mike Clarke & Frank Kee, 2019. "Social network interventions for health behaviours and outcomes: A systematic review and meta-analysis," PLOS Medicine, Public Library of Science, vol. 16(9), pages 1-25, September.
    2. Ai Koyanagi & Brendon Stubbs & Davy Vancampfort, 2018. "Correlates of sedentary behavior in the general population: A cross-sectional study using nationally representative data from six low- and middle-income countries," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-14, August.
    3. Zhang, J. & Shoham, D.A. & Tesdahl, E. & Gesell, S.B., 2015. "Network interventions on physical activity in an afterschool program: An agent-based social network study," American Journal of Public Health, American Public Health Association, vol. 105, pages 236-243.
    4. de la Haye, Kayla & Robins, Garry & Mohr, Philip & Wilson, Carlene, 2011. "How physical activity shapes, and is shaped by, adolescent friendships," Social Science & Medicine, Elsevier, vol. 73(5), pages 719-728, September.
    5. Maturo, C.C. & Cunningham, S.A., 2013. "Influence of friends on children's physical activity: A review," American Journal of Public Health, American Public Health Association, vol. 103(7), pages 23-38.
    6. Edward Ip & Qiang Zhang & Jack Rejeski & Tammy Harris & Stephen Kritchevsky, 2013. "Partially Ordered Mixed Hidden Markov Model for the Disablement Process of Older Adults," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 370-384, June.
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