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Obesity can lead to the death of at least 2.8 million people each year1, yet the rate of obesity around the world has continuously increased over the past 30 years1. Societal changes, including increased food consumption and decreased physical activity, have been determined as two of the main drivers behind the current obesity pandemic2. Examining socio-cultural factors (i.e., attitudes or perceptions of cultural groups)3 associated with food consumption and weight loss can provide important insights to guide effective interventions and a novel surveillance approach to characterize population obesity trends from sociological perspectives. The primary goal of this study is to examine socio-cultural factors associated with food consumption and weight loss by conducting sentiment analysis on related online chatters. The secondary goal is to discuss the potential implications of being exposed to these different chatters in the online environment. Scientific evidence in support of using social media to understand socio-cultural factors and its potential implications can be illustrated in two concise assertions. First, online chatters, including discussions on social media, have been shown to be an effective data source for understanding public interests4,5. Second, prolonged participation in social media has been suggested to have impacts on users6-8.

Objective: We aim to better understand socio-cultural factors (i.e., attitudes or perceptions of cultural groups) associated with food consumption and weight loss via sentiment analysis on tweets, short messages from Twitter.

Submitted by elamb on Tue, 06/18/2019 - 20:22

Overweight and obesity are recognized as one of the greatest modern public health problems1, yet worldwide prevalence of obesity has nearly doubled over the past 30 years2. As part of a strategy to control the obesity pandemic, the WHO recommends an obesity surveillance at the population level3. Empirical studies have shown the importance of social networks in obesity4 and new strategies focusing on social interactions and environments have been proposed5 to prevent the further increase in obesity prevalence. With the increasing use of the internet, online social networks, interactions, and environments (i.e., online social relational factors) deserve more attention. Nearly three- quarters of Americans go online daily6, for functions like connecting with individuals via social network sites7. Like face to face interactions, studies have suggested that social interactions and networks on the internet can influence behavior changes8. Previous studies examining social networking sites typically examine a few selected social networking sites (example studies9,10), although individuals could be members of multiple social networking sites. To better leverage online social relational factors for the purpose of characterizing and monitoring population obesity trends, we investigate weight management community members' other communities and their level of participation, a first step toward utilizing online multifactorial social interactions and environments.

Objective: We aim to better understand online social interactions and environments of individuals interested in weight management from a social media platform called Reddit.

Submitted by elamb on Tue, 06/18/2019 - 20:22

Where we live' affects 'How we live'. Information about 'how one lives' collected from the public health surveillance data such as the Behavioral Risk Factor Surveillance System (BRFSS). Neighborhood environment surrounding individuals affects their health behavior or health status are influenced as well as their own traits. Meanwhile, geographical information of subjects recruited in the health behavior surveillance data is usually aggregated at administrative levels such as a county. Even if we do not know accurate addresses of individuals, we can allocate them to the random locations where is analogous to their real home within a locality using a geo-imputation method. In this study, we assess the association between obesity and built environment by applying random property allocation.


This study aimed to assess the effects of urban physical environment on individual obesity using geographically aggregated health behavior surveillance data applying a geo-imputation method.

Submitted by elamb on Thu, 01/25/2018 - 21:40

In response to the rise in obesity rates and obesity-related healthcare costs over the past several decades, numerous organizations have implemented obesity prevention programs. The current method for evaluating the success of these programs relies largely on annual surveys such as the Centers for Disease Control and Prevention’s Behavioral Risk Factor Surveillance System (BRFSS) which provides state-by-state obesity rates. As a result, public health policy makers lack the fine-grained evaluation data needed to make timely decisions about the success of their obesity prevention programs and to allocate resources more efficiently.


We developed Persistent Health Assessment Tools, PHAT, to equip public health policy makers with more precise tools and timely information for measuring the success of obesity prevention programs. PHAT monitors social media to supplement traditional surveillance by making real-time estimates based on observations of obesity-relevant behaviors.

Submitted by Magou on Sun, 12/03/2017 - 20:32