A Google Trends Approach to Identify Distinct Diurnal and Day-of-Week Web-Based Search Patterns Related to Conjunctivitis and Other Common Eye Conditions: Infodemiology Study.

conjunctivitis diurnal eye conditions dry eye eye conditions hebdomadal infodemiology information seeking online search pink eye vision web-based search

Journal

Journal of medical Internet research
ISSN: 1438-8871
Titre abrégé: J Med Internet Res
Pays: Canada
ID NLM: 100959882

Informations de publication

Date de publication:
05 07 2022
Historique:
received: 22 01 2021
accepted: 06 05 2022
revised: 18 08 2021
pubmed: 11 5 2022
medline: 7 7 2022
entrez: 10 5 2022
Statut: epublish

Résumé

Studies suggest diurnal patterns of occurrence of some eye conditions. Leveraging new information sources such as web-based search data to learn more about such patterns could improve the understanding of patients' eye-related conditions and well-being, better inform timing of clinical and remote eye care, and improve precision when targeting web-based public health campaigns toward underserved populations. To investigate our hypothesis that the public is likely to consistently search about different ophthalmologic conditions at different hours of the day or days of week, we conducted an observational study using search data for terms related to ophthalmologic conditions such as conjunctivitis. We assessed whether search volumes reflected diurnal or day-of-week patterns and if those patterns were distinct from each other. We designed a study to analyze and compare hourly search data for eye-related and control search terms, using time series regression models with trend and periodicity terms to remove outliers and then estimate diurnal effects. We planned a Google Trends setting, extracting data from 10 US states for the entire year of 2018. The exposure was internet search, and the participants were populations who searched through Google's search engine using our chosen study terms. Our main outcome measures included cyclical hourly and day-of-week web-based search patterns. For statistical analyses, we considered P<.001 to be statistically significant. Distinct diurnal (P<.001 for all search terms) and day-of-week search patterns for eye-related terms were observed but with differing peak time periods and cyclic strengths. Some diurnal patterns represented those reported from prior clinical studies. Of the eye-related terms, "pink eye" showed the largest diurnal amplitude-to-mean ratios. Stronger signal was restricted to and peaked in mornings, and amplitude was higher on weekdays. By contrast, "dry eyes" had a higher amplitude diurnal pattern on weekends, with stronger signal occurring over a broader evening-to-morning period and peaking in early morning. The frequency of web-based searches for various eye conditions can show cyclic patterns according to time of the day or week. Further studies to understand the reasons for these variations may help supplement the current clinical understanding of ophthalmologic symptom presentation and improve the timeliness of patient messaging and care interventions.

Sections du résumé

BACKGROUND
Studies suggest diurnal patterns of occurrence of some eye conditions. Leveraging new information sources such as web-based search data to learn more about such patterns could improve the understanding of patients' eye-related conditions and well-being, better inform timing of clinical and remote eye care, and improve precision when targeting web-based public health campaigns toward underserved populations.
OBJECTIVE
To investigate our hypothesis that the public is likely to consistently search about different ophthalmologic conditions at different hours of the day or days of week, we conducted an observational study using search data for terms related to ophthalmologic conditions such as conjunctivitis. We assessed whether search volumes reflected diurnal or day-of-week patterns and if those patterns were distinct from each other.
METHODS
We designed a study to analyze and compare hourly search data for eye-related and control search terms, using time series regression models with trend and periodicity terms to remove outliers and then estimate diurnal effects. We planned a Google Trends setting, extracting data from 10 US states for the entire year of 2018. The exposure was internet search, and the participants were populations who searched through Google's search engine using our chosen study terms. Our main outcome measures included cyclical hourly and day-of-week web-based search patterns. For statistical analyses, we considered P<.001 to be statistically significant.
RESULTS
Distinct diurnal (P<.001 for all search terms) and day-of-week search patterns for eye-related terms were observed but with differing peak time periods and cyclic strengths. Some diurnal patterns represented those reported from prior clinical studies. Of the eye-related terms, "pink eye" showed the largest diurnal amplitude-to-mean ratios. Stronger signal was restricted to and peaked in mornings, and amplitude was higher on weekdays. By contrast, "dry eyes" had a higher amplitude diurnal pattern on weekends, with stronger signal occurring over a broader evening-to-morning period and peaking in early morning.
CONCLUSIONS
The frequency of web-based searches for various eye conditions can show cyclic patterns according to time of the day or week. Further studies to understand the reasons for these variations may help supplement the current clinical understanding of ophthalmologic symptom presentation and improve the timeliness of patient messaging and care interventions.

Identifiants

pubmed: 35537041
pii: v24i7e27310
doi: 10.2196/27310
pmc: PMC9297131
doi:

Types de publication

Journal Article Observational Study Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e27310

Subventions

Organisme : NEI NIH HHS
ID : P30 EY002162
Pays : United States
Organisme : NEI NIH HHS
ID : R01 EY013124
Pays : United States
Organisme : NEI NIH HHS
ID : R01 EY021558
Pays : United States
Organisme : NEI NIH HHS
ID : R01 EY024608
Pays : United States

Informations de copyright

©Michael S Deiner, Gurbani Kaur, Stephen D McLeod, Julie M Schallhorn, James Chodosh, Daniel H Hwang, Thomas M Lietman, Travis C Porco. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.07.2022.

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Auteurs

Michael S Deiner (MS)

Francis I Proctor Foundation, University of California San Francisco, San Francisco, CA, United States.
Department of Ophthalmology, University of California San Francisco, San Francisco, CA, United States.

Gurbani Kaur (G)

Department of Ophthalmology, University of California San Francisco, San Francisco, CA, United States.
School of Medicine, University of California San Francisco, San Francisco, CA, United States.

Stephen D McLeod (SD)

Francis I Proctor Foundation, University of California San Francisco, San Francisco, CA, United States.
Department of Ophthalmology, University of California San Francisco, San Francisco, CA, United States.

Julie M Schallhorn (JM)

Francis I Proctor Foundation, University of California San Francisco, San Francisco, CA, United States.
Department of Ophthalmology, University of California San Francisco, San Francisco, CA, United States.

James Chodosh (J)

Department of Ophthalmology, Harvard Medical School, Boston, MA, United States.
Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, United States.

Daniel H Hwang (DH)

Stanford University, San Mateo, CA, United States.
The Nueva School, San Mateo, CA, United States.

Thomas M Lietman (TM)

Francis I Proctor Foundation, University of California San Francisco, San Francisco, CA, United States.
Department of Ophthalmology, University of California San Francisco, San Francisco, CA, United States.
Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, United States.
Global Health Sciences, University of California San Francisco, San Francisco, CA, United States.

Travis C Porco (TC)

Francis I Proctor Foundation, University of California San Francisco, San Francisco, CA, United States.
Department of Ophthalmology, University of California San Francisco, San Francisco, CA, United States.
Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, United States.
Global Health Sciences, University of California San Francisco, San Francisco, CA, United States.

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