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?
This research will determine the extent to which people's social network position and their ties to others affect and are affected by two important health-related behaviors - physical activity and sleep habits - that are known to have effects on health outcomes such as specific types of cancer, cardiovascular diseases, obesity, cognitive functioning, and life expectancy. We will collect data on both networks and behaviors unobtrusively using remote, mobile always-on sensors: smartphones to capture information on communicative interactions and social ties and health monitor armbands which people wear all the time to capture information on physical activity and sleep habits. With these two streams of data we will be able to (1) test theories about the mechanisms linking networks and behaviors and (2) assess the extent to which social influence processes within networks lead to changes in these two important health-related behaviors.
