Evidence map›Paper›PMID 39948580›Full record

ArticleBMC pulmonary medicine2025

Public interest in online searching of asthma information: insights from a Google trends analysis.

Marsa Gholamzadeh, Mehrnaz Asadi Gharabaghi, Hamidreza Abtahi

Abstract read
In one paragraph

Article in BMC pulmonary medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Marsa GholamzadehHealth Information Management and Medical Informatics Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.ORCID https://orcid.org/0000-0001-6781-9342
Mehrnaz Asadi GharabaghiDepartment of Pulmonary Medicine, Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran.ORCID http://orcid.org/0000-0003-0852-1532
Hamidreza AbtahiPulmonary and Critical Care Department, Thoracic Research Center, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Qarib Ave, Keshavarz Blv, Tehran, Iran. hrabtahi@tums.ac.ir.ORCID http://orcid.org/0000-0002-1111-0497

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGoogle Trends (GT) is a free tool that provides insights into the public's interest and information-seeking behavior on specific topics. In this study, we utilized GT data on patients' search history to better understand their questions and information needs regarding asthma.

methodsWe extracted the relative GT search volume (RSV) for keywords associated with asthma to explore information-seeking behaviors and assess internet search patterns regarding asthma disease from 2004 to 2024 in both English and Persian languages. In addition, a correlation analysis was conducted to assess terms correlated with asthma searches. Then, the AutoRegressive predictive models were developed to estimate future patterns of asthma-related searches and the information needs of individuals with asthma.

resultsThe analysis revealed that the mean total RSV for asthma-related keywords over the 20-year period was 41.79 ± 6.07. The researchers found that while asthma-related search volume has shown a consistent upward trend in Persian-speaking countries over the last decade, English-speaking countries have experienced less variability in such searches except for a spike during the COVID-19 pandemic. The correlation analysis of related subjects showed that "air pollution", "infection", and "insomnia" have a positive correlation with asthma. Developing AutoRegressive predictive models on retrieved Google Trends data revealed a seasonal pattern in global asthma-related search interest. In contrast, the models forecasted a growing increase in information-seeking behaviors regarding asthma among Persian-speaking patients over the coming decades.

conclusionsThere are significant differences in how people search for and access asthma information based on their language and regional context. In English-speaking countries, searches tend to focus on broader asthma-related topics like pollution and infections, likely due to the availability of comprehensive asthma resources. In contrast, Persian speakers prioritize understanding specific aspects of asthma-like symptoms, medications, and complementary treatments. To address these divergent information needs, health organizations should tailor content to these divergent needs.

Indexed as

AsthmaConsumer Health InformationInformation Seeking BehaviorInternetSearch EngineCOVID-19HumansIranAsthmaGoogle trendPublic interestRelative search volumeTime series analysis

Identifiers

PMID39948580
PMCPMC11827464

What OpenQuestion holds

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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.