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Network analysis of the NetHealth data: exploring co-evolution of individuals’ social network positions and physical activities
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 a...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6223883/ https://www.ncbi.nlm.nih.gov/pubmed/30465021 http://dx.doi.org/10.1007/s41109-018-0103-2 |
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author | Liu, Shikang Hachen, David Lizardo, Omar Poellabauer, Christian Striegel, Aaron Milenković, Tijana |
author_facet | Liu, Shikang Hachen, David Lizardo, Omar Poellabauer, Christian Striegel, Aaron Milenković, Tijana |
author_sort | Liu, Shikang |
collection | PubMed |
description | 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 MATERIAL: The online version of this article (10.1007/s41109-018-0103-2) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6223883 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-62238832018-11-19 Network analysis of the NetHealth data: exploring co-evolution of individuals’ social network positions and physical activities Liu, Shikang Hachen, David Lizardo, Omar Poellabauer, Christian Striegel, Aaron Milenković, Tijana Appl Netw Sci Research 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 MATERIAL: The online version of this article (10.1007/s41109-018-0103-2) contains supplementary material, which is available to authorized users. Springer International Publishing 2018-11-02 2018 /pmc/articles/PMC6223883/ /pubmed/30465021 http://dx.doi.org/10.1007/s41109-018-0103-2 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Research Liu, Shikang Hachen, David Lizardo, Omar Poellabauer, Christian Striegel, Aaron Milenković, Tijana Network analysis of the NetHealth data: exploring co-evolution of individuals’ social network positions and physical activities |
title | Network analysis of the NetHealth data: exploring co-evolution of individuals’ social network positions and physical activities |
title_full | Network analysis of the NetHealth data: exploring co-evolution of individuals’ social network positions and physical activities |
title_fullStr | Network analysis of the NetHealth data: exploring co-evolution of individuals’ social network positions and physical activities |
title_full_unstemmed | Network analysis of the NetHealth data: exploring co-evolution of individuals’ social network positions and physical activities |
title_short | Network analysis of the NetHealth data: exploring co-evolution of individuals’ social network positions and physical activities |
title_sort | network analysis of the nethealth data: exploring co-evolution of individuals’ social network positions and physical activities |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6223883/ https://www.ncbi.nlm.nih.gov/pubmed/30465021 http://dx.doi.org/10.1007/s41109-018-0103-2 |
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