NetHealth: Modeling the Co-Evolution of Social Networks and Health Behaviors

描述：这个项目的目的是在两年的时间里收集人们的社会网络和健康行为的连续纵向数据，以便(1)检验社会网络和人类行为联系机制的理论，(2)评估社会影响过程导致健康相关行为变化的程度。评估社会影响在社会网络中的作用程度，对于设计未来的干预措施至关重要，这些干预措施可以利用社会网络的力量来减少不健康行为的发生率，并增加健康行为的流行。然而，从经验上确定社会影响的重要性是非常困难的。除了社会传染之外，还有其他机制可以发生观察到的聚类（即相似的人之间的联系）：自我选择（与相似的人形成联系），共同暴露于并发的外源因素，选择性回避，以及不表现出特征匹配的联系的高衰减率。从经验上判断这些相互竞争的过程需要高有效性、细粒度的纵向数据，这些数据是关于人们的社交网络及其与健康相关的行为方面随时间变化的。该项目将通过创新地使用两种移动、远程、永远在线、不引人注目的传感器系统来收集这些数据：智能手机和其他智能设备，用于捕捉交流互动和社会关系的信息，以及健康监测臂带，用于捕捉身体活动（PA）和睡眠习惯（SH）的信息。500名即将入学的大学生将被要求在他们的智能设备上安装监测应用程序，并在抵达校园时获得一个健康监测臂带，以便随时佩戴。从手机和其他智能设备获得的谁与谁通信的数据，以及从臂章获得的PA和SH数据，将使我们能够绘制出社交网络以及其中PA和SH行为的共同进化。因为学生们被随机分配到同性宿舍和房间里，而且在到达之前几乎没有社会关系，所以这是一个观察同性恋出现的想法群体

DESCRIPTION: The purpose of this project is to collect continuous longitudinal data over a two year period on both people's social networks and health behaviors in order to (1) test theories about the mechanisms linking social networks and human behavior, and (2) assess the extent to which social influence processes lead to changes in health-related behaviors. Assessing the extent to which social influence operates in social networks is critical for devising future interventions that could harness the power of social networks to reduce incidences of unhealthy behaviors and increase the prevalence of healthier ones. However empirically determining how important social influence is has turned out to be very difficult. There are other mechanisms besides social contagion through which the observed clustering (i.e. ties among similar people) can occur: self-selection (forming ties with similar others), joint exposure to concurrent exogenous factors, selective avoidance, and high decay rates for ties that do not exhibit trait matching. Empirically adjudicating between these competing processes requires high-validity, fine-grained, longitudinal data on changes over time in people's social networks and their health-related behavioral sides. This project will collect such data through the innovative use of two mobile, remote, always-on unobtrusive sensor systems: smartphones and other smart devices to capture information on communicative interactions and social ties, and health monitor armbands to capture information on physical activity (PA) and sleep habits (SH). Five hundred incoming college students will be asked to install monitoring applications on their smart devices and will receive a health monitor armband upon arrival on campus to wear the armband at all times. Data about who communicates with who obtained from the phones and other smart devices, and data on PA and SH obtained from the armbands will allow us to map out the co-evolution of a social network and PA and SH behaviors within it. Because students are randomly allocated to same-sex residence halls and rooms within them, and because there are few social ties prior to arrival, this is an idea population in which to observe the emergence of a social network. With these data we will answer fundamental questions about networks and behavior. Does who you know (a person's position in a social network) determine what you do (for instance, how physical active a person is)? Or does what you do determine who you know? What happens when two people with different PA and/or SH form a social tie? Are they likely to become similar, and if so, is it because the less active (poorer sleeper) becomes more active (a healthier sleepier) or vice a versa? Alternatively, when there are health behavior differences, is the tie likely to die quickly and never get a chance to strengthen enough so that influence processes can begin to come into play?