Evidence map›Paper›PMID 35721836›Full record

ArticlePatterns (New York, N.Y.)2022

Diet during the COVID-19 pandemic: An analysis of Twitter data.

Mark A Hernandez, Shagun Modi, Kanisha Mittal, Pallavi Dwivedi, Quynh C Nguyen, Nina L Cesare, Elaine O Nsoesie

Abstract read
In one paragraph

Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Mark A HernandezBoston University School of Public Health, Boston, MA 02118, USA.
Shagun ModiBoston University School of Public Health, Boston, MA 02118, USA.
Kanisha MittalBoston University School of Public Health, Boston, MA 02118, USA.
Pallavi DwivediDepartment of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD 20740, USA.
Quynh C NguyenDepartment of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD 20740, USA.
Nina L CesareBoston University School of Public Health, Boston, MA 02118, USA.
Elaine O NsoesieBoston University School of Public Health, Boston, MA 02118, USA.

Funding

Rosie the Chatbot: Leveraging Automated and Personalized Health Information Communication to Reduce Disparities in Maternal and Child HealthR01MD016037 · NIMHD · UNIV OF MARYLAND, COLLEGE PARK · PI NGUYEN, THU, NORELL, ELIZABETH MARIE · 2021 to 2025
$3.3M
Neighborhood Looking Glass: 360 Degree Automated Characterization of the Built Environment for Neighborhood Effects ResearchR01LM012849 · NLM · UNIV OF MARYLAND, COLLEGE PARK · PI NGUYEN, QUYNH · 2018 to 2021
$1.3M
NIMHD NIH HHS R01 MD016037NLM NIH HHS R01 LM012849
6 · The paper itself

Abstract

In this study, we measured the association between county characteristics and changes in healthy-food, fast-food, and alcohol tweets during the COVID-19 pandemic in the United States. Our analytic dataset consisted of 1,282,316 geotagged tweets that referenced food consumption posted before (63.2%) and during (36.8%) the pandemic and included all US states. We found the share of healthy-food tweets increased by 20.5% during the pandemic compared with pre-pandemic, while fast-food and alcohol tweets decreased by 9.4% and 11.4%, respectively. We also observed that time spent at home and more grocery stores per capita were associated with increased odds of healthy-food tweets and decreased odds of fast-food tweets. More liquor stores per capita was associated with increased odds of alcohol tweets. Our results highlight the potential impact of the pandemic on nutrition and alcohol consumption and the association between the built environment and health behaviors.

Indexed as

alcohol usebehavior changesbuilt environmentCOVID-19dietTwitter analysis

Identifiers

PMID35721836
PMCPMC9197791

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.