Evidence map›Paper›PMID 34290916›Full record

ArticleCureus2021

Analyzing Public Interest in Metabolic Health-Related Search Terms During COVID-19 Using Google Trends.

Alec D McCarthy, Daniel McGoldrick

Abstract read
In one paragraph

Article in Cureus, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

2 authors.

Alec D McCarthyDepartment of Surgery, University of Nebraska Medical Center, Omaha, USA.
Daniel McGoldrickDepartment of Computer Science, California State University, Monterey Bay, Seaside, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background In late 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged as a novel virus and initiated a series of events that culminated in the global coronavirus disease 2019 (COVID-19) pandemic. Throughout 2020 and the first half of 2021, massive investigational efforts towards identifying, treating, preventing, and slowing the spread of COVID-19 were carried out. Several predictors for clinical outcomes relating to metabolic health were identified. Aim and methods This study aimed to investigate how public interest in search terms associated with metabolic health has changed throughout and during the course of the COVID-19 pandemic. Google Trends was utilized as a tool to gather and compare public interest data in a variety of search phrases. The relative search values were plotted over time, compared pre-and post-COVID-19, analyzed for correlation, assessed for trend directionality, and checked for trend inclusion. Results The public interest measured by relative search volume in "metabolic health," "exercise," "home exercise," "health," and "how to improve fitness" significantly increased from pre- to post-COVID-19 pandemic onset while "diet" and "fitness" significantly decreased. The search terms "COVID" and "coronavirus" made up more than 95% of screen queries incorporating COVID-19. During the COVID-19 pandemic, "diabetes" and "weight loss" had the most significant increases in search volume. Conclusions Given the changes in public interest observed throughout the course of the COVID-19 pandemic, it is clear that the association between metabolic health and COVID-19 is being successfully disseminated to the public. However, these changes also warrant increased public education surrounding diet and fitness to align public interest with measures proven to improve the clinical outcomes of COVID-19.

Indexed as

coronaviruscovid-19google trendsmetabolic healthmetabolismobesitypublic interestsars-cov-2scientific communicationsocial data

Identifiers

PMID34290916
PMCPMC8288585

What OpenQuestion holds

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LicenceCC BY
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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.