Twitter-Derived Social Neighborhood Characteristics and Individual-Level Cardiometabolic Outcomes: Cross-Sectional Study in a Nationally Representative Sample.


Journal

JMIR public health and surveillance
ISSN: 2369-2960
Titre abrégé: JMIR Public Health Surveill
Pays: Canada
ID NLM: 101669345

Informations de publication

Date de publication:
18 08 2020
Historique:
received: 29 01 2020
accepted: 27 05 2020
revised: 26 04 2020
entrez: 19 8 2020
pubmed: 19 8 2020
medline: 28 7 2021
Statut: epublish

Résumé

Social media platforms such as Twitter can serve as a potential data source for public health research to characterize the social neighborhood environment. Few studies have linked Twitter-derived characteristics to individual-level health outcomes. This study aims to assess the association between Twitter-derived social neighborhood characteristics, including happiness, food, and physical activity mentions, with individual cardiometabolic outcomes using a nationally representative sample. We collected a random 1% of the geotagged tweets from April 2015 to March 2016 using Twitter's Streaming Application Interface (API). Twitter-derived zip code characteristics on happiness, food, and physical activity were merged to individual outcomes from restricted-use National Health and Nutrition Examination Survey (NHANES) with residential zip codes. Separate regression analyses were performed for each of the neighborhood characteristics using NHANES 2011-2016 and 2007-2016. Individuals living in the zip codes with the two highest tertiles of happy tweets reported BMI of 0.65 (95% CI -1.10 to -0.20) and 0.85 kg/m Twitter-derived social neighborhood characteristics were associated with individual-level obesity and hypertension in a nationally representative sample of US adults. Twitter data could be used for capturing neighborhood sociocultural influences on chronic conditions and may be used as a platform for chronic outcomes prevention.

Sections du résumé

BACKGROUND
Social media platforms such as Twitter can serve as a potential data source for public health research to characterize the social neighborhood environment. Few studies have linked Twitter-derived characteristics to individual-level health outcomes.
OBJECTIVE
This study aims to assess the association between Twitter-derived social neighborhood characteristics, including happiness, food, and physical activity mentions, with individual cardiometabolic outcomes using a nationally representative sample.
METHODS
We collected a random 1% of the geotagged tweets from April 2015 to March 2016 using Twitter's Streaming Application Interface (API). Twitter-derived zip code characteristics on happiness, food, and physical activity were merged to individual outcomes from restricted-use National Health and Nutrition Examination Survey (NHANES) with residential zip codes. Separate regression analyses were performed for each of the neighborhood characteristics using NHANES 2011-2016 and 2007-2016.
RESULTS
Individuals living in the zip codes with the two highest tertiles of happy tweets reported BMI of 0.65 (95% CI -1.10 to -0.20) and 0.85 kg/m
CONCLUSIONS
Twitter-derived social neighborhood characteristics were associated with individual-level obesity and hypertension in a nationally representative sample of US adults. Twitter data could be used for capturing neighborhood sociocultural influences on chronic conditions and may be used as a platform for chronic outcomes prevention.

Identifiants

pubmed: 32808935
pii: v6i3e17969
doi: 10.2196/17969
pmc: PMC7485998
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

e17969

Subventions

Organisme : NIEHS NIH HHS
ID : K01 ES025433
Pays : United States
Organisme : NICHD NIH HHS
ID : P2C HD041041
Pays : United States
Organisme : NLM NIH HHS
ID : R01 LM012849
Pays : United States

Informations de copyright

©Dina Huang, Yuru Huang, Sahil Khanna, Pallavi Dwivedi, Natalie Slopen, Kerry M Green, Xin He, Robin Puett, Quynh Nguyen. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 18.08.2020.

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Auteurs

Dina Huang (D)

Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.

Yuru Huang (Y)

Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.

Sahil Khanna (S)

A. James Clark School of Engineering, University of Maryland, College Park, MD, United States.

Pallavi Dwivedi (P)

Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.

Natalie Slopen (N)

Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.

Kerry M Green (KM)

Department of Behavioral and Community Health, University of Maryland School of Public Health, College Park, MD, United States.

Xin He (X)

Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.

Robin Puett (R)

Maryland Institute for Applied Environmental Health, University of Maryland School of Public Health, College Park, MD, United States.

Quynh Nguyen (Q)

Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.

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