Social network structure is predictive of health and wellness
Social network structure is predictive of health and wellness
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DOI：
10.1371/journal.pone.0217264
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发表时间：
2019-06-06
期刊：
影响因子：
3.7
通讯作者：
Chawla, Nitesh V.
中科院分区：
文献类型：
--
作者：
Lin, Suwen;Faust, Louis;Chawla, Nitesh V.
社交网络会影响与健康相关的行为，如肥胖和吸烟。尽管研究人员将社交网络作为影响和行为扩散的驱动力进行了研究，但人们对社交网络本身的结构或拓扑结构如何影响个人的健康行为和健康状态知之甚少。在这篇文章中，我们调查了社交网络的结构或拓扑是否为个人的健康和健康提供了额外的洞察力和可预测性。我们开发了一种名为网络驱动的健康预测器(Netcare)的方法，该方法利用了代表社交网络结构的功能。使用圣母大学NetHealth研究中注册的学生的大型纵向数据集，我们发现Netcare方法比使用人口统计和身体属性的基线模型的整体预测性能提高了38%、65%、55%和54%，包括压力、幸福感、积极态度和自我评估的健康状况。
Social networks influence health-related behavior, such as obesity and smoking. While researchers have studied social networks as a driver for diffusion of influences and behavior, it is less understood how the structure or topology of the network, in itself, impacts an individual's health behavior and wellness state. In this paper, we investigate whether the structure or topology of a social network offers additional insight and predictability on an individual's health and wellness. We develop a method called the Network-Driven health predictor (NetCARE) that leverages features representative of social network structure. Using a large longitudinal data set of students enrolled in the NetHealth study at the University of Notre Dame, we show that the NetCARE method improves the overall prediction performance over the baseline models that use demographics and physical attributes-by 38%, 65%, 55%, and 54% for the wellness states-stress, happiness, positive attitude, and self assessed health-considered in this paper.