Engineering online and in-person social networks to sustain physical activity: application of a conceptual model
1 Departments of Medicine and Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
2 Department of Family and Preventive Medicine, University of California, San Diego, San Diego, CA, USA
3 Departments of Medicine and Public Health Sciences, Penn State Milton S. Hershey Medical Center, Hershey, PA, USA
4 Departments of Medicine and Cellular and Molecular Physiology, Penn State College of Medicine, Hershey, PA, USA
5 Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
6 School of Public Health, University of Sydney, Sydney, Australia
7 Departments of Sociology, Anthropology, and Demography, Penn State University, University Park, PA, USA
8 Department of Kinesiology, Penn State University, University Park, PA, USA
9 Department of Humanities, Penn State College of Medicine, Hershey, PA, USA
10 Graduate School of Public Health, San Diego State University, San Diego, CA, USA
BMC Public Health 2013, 13:753 doi:10.1186/1471-2458-13-753Published: 14 August 2013
High rates of physical inactivity compromise the health status of populations globally. Social networks have been shown to influence physical activity (PA), but little is known about how best to engineer social networks to sustain PA. To improve procedures for building networks that shape PA as a normative behavior, there is a need for more specific hypotheses about how social variables influence PA. There is also a need to integrate concepts from network science with ecological concepts that often guide the design of in-person and electronically-mediated interventions. Therefore, this paper: (1) proposes a conceptual model that integrates principles from network science and ecology across in-person and electronically-mediated intervention modes; and (2) illustrates the application of this model to the design and evaluation of a social network intervention for PA.
A conceptual model for engineering social networks was developed based on a scoping literature review of modifiable social influences on PA. The model guided the design of a cluster randomized controlled trial in which 308 sedentary adults were randomly assigned to three groups: WalkLink+: prompted and provided feedback on participants’ online and in-person social-network interactions to expand networks for PA, plus provided evidence-based online walking program and weekly walking tips; WalkLink: evidence-based online walking program and weekly tips only; Minimal Treatment Control: weekly tips only. The effects of these treatment conditions were assessed at baseline, post-program, and 6-month follow-up. The primary outcome was accelerometer-measured PA. Secondary outcomes included objectively-measured aerobic fitness, body mass index, waist circumference, blood pressure, and neighborhood walkability; and self-reported measures of the physical environment, social network environment, and social network interactions. The differential effects of the three treatment conditions on primary and secondary outcomes will be analyzed using general linear modeling (GLM), or generalized linear modeling if the assumptions for GLM cannot be met.
Results will contribute to greater understanding of how to conceptualize and implement social networks to support long-term PA. Establishing social networks for PA across multiple life settings could contribute to cultural norms that sustain active living.