Abstract
Understanding the relationship between individuals’ social networks and health could help devise public health interventions for reducing incidence of unhealthy behaviors or increasing prevalence of healthy ones. In this context, we explore the co-evolution of individuals’ social network positions and physical activities. We are able to do so because the NetHealth study at the University of Notre Dame has generated both high-resolution longitudinal social network (e.g., SMS) data and high-resolution longitudinal health-related behavioral (e.g., Fitbit physical activity) data. We examine trait differences between (i) users whose social network positions (i.e., centralities) change over time versus those whose centralities remain stable, (ii) users whose Fitbit physical activities change over time versus those whose physical activities remain stable, and (iii) users whose centralities and their physical activities co-evolve, i.e., correlate with each other over time. We find that centralities of a majority of all nodes change with time. These users do not show any trait difference compared to time-stable users. However, if out of all users whose centralities change with time we focus on those whose physical activities also change with time, then the resulting users are more likely to be introverted than time-stable users. Moreover, users whose centralities and physical activities both change with time and whose evolving centralities are significantly correlated (i.e., co-evolve) with evolving physical activities are more likely to be introverted as well as anxious compared to those users who are time-stable and do not have a co-evolution relationship. Our network analysis framework reveals several links between individuals’ social network structure, health-related behaviors, and the other (e.g., personality) traits. In the future, our study could lead to development of a predictive model of social network structure from behavioral/trait information and vice versa.
Highlights
Problem statement and related work Individuals’ health is closely related to their social networks (Latkin and Knowlton 2015; Perkins et al 2015; Pastor-Satorras et al 2015; Valente and Pitts 2017; Cobb et al 2010)
We find that the longer the break period, the fewer nodes are participating in the largest connected component (LCC): around 75% of all users are in the LCCs during the 1-week long fall and spring breaks, around 60% of all users are in the LCCs during the several weeks long winter break, and only around 50% of all users are in the LCCs during the 3-month long summer break (Fig. 2)
In terms of global properties, we find that: 1) several commonly used global properties are stable over time, 2) the best fitting model for each snapshot is the same, which is Geometric random graphs (GEO), and 3) consecutive snapshots are more similar than non-consecutive snapshots with respect to Edge overlapping (EO) and centrality-based measures
Summary
Problem statement and related work Individuals’ health is closely related to their social networks (Latkin and Knowlton 2015; Perkins et al 2015; Pastor-Satorras et al 2015; Valente and Pitts 2017; Cobb et al 2010) Understanding this relationship could help devise network-based strategies to reduce the incidence of unhealthy behaviors or increase the prevalence of healthy ones. It was shown that people with specific traits, such as obesity, tend to have different (higher or lower, depending on the trait) centrality values in a static social network (Strauss and Pollack 2003) As another example, individuals’ positions in a static social network were shown to be correlated with their personalities (Staiano et al 2012; Klein et al 2004), creativities (Gloor et al 2011; Perry-Smith and Shalley 2003), and self-reported health (Youm et al 2014)
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