Network analysis of the NetHealth data: exploring co-evolution of individuals’ social network positions and physical activities

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.Electronic supplementary materialThe online version of this article (10.1007/s41109-018-0103-2) contains supplementary material, which is available to authorized users.

The power of dynamic social networks to predict individuals’ mental health

“…We already produced a collection of manuscripts on various research questions related to various dimensions of the NetHealth data [27,28]. This study is a novel addition to that collection.…”

“…This study is a novel addition to that collection. To be able to draw conclusions across the different dimensions, we match the social interaction and mental health trait data closely between this current study and our previous studies [27,28]. This includes the choices of the study time period and pool of individuals, as follows.…”

“…In this study, we focus on the role of individuals' social interactions on their health. For example, individuals' health-related traits, such as obesity, smoking, depression, physical activities, heart rates, self-perceived health, happiness, or stress can spread through social interactions [9,10,40] or are correlated with the individuals' positions (e.g., centralities) in their social network [20,26,27,31,43,48].…”

“…On the other hand, network studies either use the same data as non-network studies but first process the raw data into a network that captures relationships between entities (e.g., by linking two individuals if their physical activity profiles are similar), or they use explicit social network data (e.g., friendships between individuals). Then, they extract network-based features (e.g., an individual's centralities in the network) and input those features into off-the-shelf classifiers [26,27,31,46]. Since networks capture complex wirings between entities, network studies are expected to be advantageous.…”

“…Among network studies (including those that did not necessarily focus on mental health), a majority only explored the existence of a potential "correlation" between individuals' social networks and their traits (e.g., whether individuals who have different personality traits tend to have different centrality values). That is, they did not build any predictive models [8,9,10,20,21,24,27,31,40,42,43,48], which is what we aim to do here. Plus, a majority of these did not focus on mental health, which we do in this study.…”