Rovniak Liza S, Sallis James F, Kraschnewski Jennifer L, Sciamanna Christopher N, Kiser Elizabeth J, Ray Chester A, Chinchilli Vernon M, Ding Ding, Matthews Stephen A, Bopp Melissa, George Daniel R, Hovell Melbourne F
Departments of Medicine and Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA.
BMC Public Health. 2013 Aug 14;13:753. doi: 10.1186/1471-2458-13-753.
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.
METHODS/DESIGN: 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.
ClinicalTrials.gov NCT01142804.
全球范围内，缺乏身体活动的高比例人群损害了整体健康状况。社交网络已被证明会影响身体活动（PA），但对于如何优化社交网络以维持身体活动，我们却知之甚少。为改进构建社交网络的程序，使身体活动成为一种规范行为，需要对社会变量如何影响身体活动提出更具体的假设。此外，还需要将网络科学的概念与生态概念相结合，这些生态概念通常指导面对面和电子媒介干预的设计。因此，本文：（1）提出一个概念模型，该模型整合了网络科学和生态学的原理，涵盖面对面和电子媒介干预模式；（2）阐述该模型在身体活动社交网络干预设计与评估中的应用。
方法/设计：基于对可改变的社会因素对身体活动影响的文献综述，开发了一个构建社交网络的概念模型。该模型指导了一项整群随机对照试验的设计，其中308名久坐不动的成年人被随机分为三组：WalkLink +组：针对参与者的线上和线下社交网络互动给予提示并提供反馈，以拓展身体活动网络，同时提供基于证据的在线步行计划和每周步行小贴士；WalkLink组：仅提供基于证据的在线步行计划和每周小贴士；最小治疗对照组：仅提供每周小贴士。在基线、项目结束后和6个月随访时评估这些治疗条件的效果。主要结局是通过加速度计测量的身体活动。次要结局包括客观测量的有氧适能、体重指数、腰围、血压和邻里步行便利性；以及自我报告的身体环境、社交网络环境和社交网络互动指标。将使用一般线性模型（GLM）分析三种治疗条件对主要和次要结局的差异效应，如果GLM的假设不成立，则使用广义线性模型。
研究结果将有助于更深入地理解如何构建和实施社交网络以支持长期身体活动。在多种生活场景中建立身体活动社交网络有助于形成维持积极生活方式的文化规范。
ClinicalTrials.gov NCT01142804。
