van Woudenberg Thabo J, Simoski Bojan, Fernandes de Mello Araújo Eric, Bevelander Kirsten E, Burk William J, Smit Crystal R, Buijs Laura, Klein Michel, Buijzen Moniek
Behavioural Science Institute, Radboud University, Nijmegen, Netherlands.
Social AI Group, Vrije Universiteit, Amsterdam, Netherlands.
J Med Internet Res. 2019 Aug 5;21(8):e12914. doi: 10.2196/12914.
Social network interventions targeted at children and adolescents can have a substantial effect on their health behaviors, including physical activity. However, designing successful social network interventions is a considerable research challenge. In this study, we rely on social network analysis and agent-based simulations to better understand and capitalize on the complex interplay of social networks and health behaviors. More specifically, we investigate criteria for selecting influence agents that can be expected to produce the most successful social network health interventions.
The aim of this study was to test which selection criterion to determine influence agents in a social network intervention resulted in the biggest increase in physical activity in the social network. To test the differences among the selection criteria, a computational model was used to simulate different social network interventions and observe the intervention's effect on the physical activity of primary and secondary school children within their school classes. As a next step, this study relied on the outcomes of the simulated interventions to investigate whether social network interventions are more effective in some classes than others based on network characteristics.
We used a previously validated agent-based model to understand how physical activity spreads in social networks and who was influencing the spread of behavior. From the observed data of 460 participants collected in 26 school classes, we simulated multiple social network interventions with different selection criteria for the influence agents (ie, in-degree centrality, betweenness centrality, closeness centrality, and random influence agents) and a control condition (ie, no intervention). Subsequently, we investigated whether the detected variation of an intervention's success within school classes could be explained by structural characteristics of the social networks (ie, network density and network centralization).
The 1-year simulations showed that social network interventions were more effective compared with the control condition (beta=.30; t100=3.23; P=.001). In addition, the social network interventions that used a measure of centrality to select influence agents outperformed the random influence agent intervention (beta=.46; t100=3.86; P<.001). Also, the closeness centrality condition outperformed the betweenness centrality condition (beta=.59; t100=2.02; P=.046). The anticipated interaction effects of the network characteristics were not observed.
Social network intervention can be considered as a viable and promising intervention method to promote physical activity. We demonstrated the usefulness of applying social network analysis and agent-based modeling as part of the social network interventions' design process. We emphasize the importance of selecting the most successful influence agents and provide a better understanding of the role of network characteristics on the effectiveness of social network interventions.
针对儿童和青少年的社交网络干预措施可能会对他们的健康行为产生重大影响，包括身体活动。然而，设计成功的社交网络干预措施是一项颇具挑战性的研究任务。在本研究中，我们依靠社交网络分析和基于主体的模拟，以更好地理解并利用社交网络与健康行为之间的复杂相互作用。更具体地说，我们研究了选择能够产生最成功社交网络健康干预效果的影响主体的标准。
本研究的目的是测试在社交网络干预中确定影响主体的哪种选择标准能使社交网络中的身体活动增加最多。为了测试各选择标准之间的差异，我们使用了一个计算模型来模拟不同的社交网络干预，并观察干预对中小学班级内学生身体活动的影响。下一步，本研究依据模拟干预的结果，基于网络特征来调查社交网络干预在某些班级是否比其他班级更有效。
我们使用了一个先前经过验证的基于主体的模型，以了解身体活动在社交网络中是如何传播的，以及谁在影响行为的传播。从26个班级收集的460名参与者的观察数据中，我们模拟了多种社交网络干预，针对影响主体采用不同的选择标准（即入度中心性、介数中心性、接近中心性和随机影响主体）以及一个对照条件（即不进行干预）。随后，我们调查了在班级中检测到的干预成功差异是否可以由社交网络的结构特征（即网络密度和网络中心性）来解释。
为期1年的模拟显示，与对照条件相比，社交网络干预更有效（β = 0.30；t100 = 3.23；P = 0.001）。此外，使用中心性度量来选择影响主体 的社交网络干预优于随机影响主体干预（β = 0.46；t100 = 3.86；P < 0.001）。而且，接近中心性条件优于介数中心性条件（β = 0.59；t100 = 2.02；P = 0.046）。未观察到网络特征的预期交互作用。
社交网络干预可被视为促进身体活动的一种可行且有前景的干预方法。我们证明了将社交网络分析和基于主体的建模作为社交网络干预设计过程的一部分的有用性。我们强调选择最成功影响主体的重要性，并更好地理解网络特征对社交网络干预有效性的作用。
