Literature DB >> 28660638

Using Search Engines to Investigate Shared Migraine Experiences.

Sara M Burns1, Dana P Turner1, Katherine E Sexton1, Hao Deng1, Timothy T Houle1.   

Abstract

OBJECTIVES: To investigate migraine patterns in the United States using Google search data and utilize this information to better understand societal-level trends. Additionally, we aimed to evaluate time-series relationships between migraines and social factors.
BACKGROUND: Extensive research has been done on clinical factors associated with migraines, yet population-level social factors have not been widely explored. Migraine internet search data may provide insight into migraine trends beyond information that can be gleaned from other sources.
METHODS: In this longitudinal analysis of open access data, we performed a time-series analysis in which about 12 years of Google Trends data (January 1, 2004 to August 15, 2016) were assessed. Data points were captured at a daily level and Google's 0-100 adjusted scale was used as the primary outcome to enable the comparison of relative popularity in the migraine search term. We hypothesized that the volume of relative migraine Google searches would be affected by societal aspects such as day of the week, holidays, and novel social events.
RESULTS: Several recurrent social factors that drive migraine searches were identified. Of these, day of the week had the most significant impact on the volume of Google migraine searches. On average, Mondays accumulated 13.31 higher relative search volume than Fridays (95% CI: 11.12-15.51, P ≤ .001). Surprisingly, holidays were associated with lower relative migraine search volumes. Christmas Day had 13.84 lower relative search volumes (95% CI: 6.26-21.43, P ≤ .001) and Thanks giving had 20.18 lower relative search volumes (95% CI: 12.55-27.82, P ≤ .001) than days that were not holidays. Certain novel social events and extreme weather also appear to be associated with relative migraine Google search volume.
CONCLUSIONS: Social factors play a crucial role in explaining population level migraine patterns, and thus, warrant further exploration.
© 2017 American Headache Society.

Entities:  

Keywords:  ARIMA; Google Trends; headache; migraine; social predictors; time-series

Mesh:

Year:  2017        PMID: 28660638      PMCID: PMC5937702          DOI: 10.1111/head.13130

Source DB:  PubMed          Journal:  Headache        ISSN: 0017-8748            Impact factor:   5.887


  21 in total

1.  Migraine prevalence, socioeconomic status, and social causation.

Authors:  Walter F Stewart; Jason Roy; Richard B Lipton
Journal:  Neurology       Date:  2013-08-29       Impact factor: 9.910

2.  Social networks, social media, and social diseases.

Authors:  Enrico Coiera
Journal:  BMJ       Date:  2013-05-22

3.  Effect of population-based interventions on laboratory utilization: a time-series analysis.

Authors:  C van Walraven; V Goel; B Chan
Journal:  JAMA       Date:  1998-12-16       Impact factor: 56.272

4.  Tweeting about pain: comparing self-reported toothache experiences with those of backaches, earaches and headaches.

Authors:  Kristina Ahlwardt; Natalie Heaivilin; Jennifer Gibbs; Jens Page; Barbara Gerbert; Janice Y Tsoh
Journal:  J Am Dent Assoc       Date:  2014-07       Impact factor: 3.634

Review 5.  Social Media Use in Chronic Disease: A Systematic Review and Novel Taxonomy.

Authors:  Rajesh Patel; Tammy Chang; S Ryan Greysen; Vineet Chopra
Journal:  Am J Med       Date:  2015-07-06       Impact factor: 4.965

6.  Nighttime snacking, stress, and migraine activity.

Authors:  Dana P Turner; Todd A Smitherman; Donald B Penzien; John A H Porter; Vincent T Martin; Timothy T Houle
Journal:  J Clin Neurosci       Date:  2013-10-14       Impact factor: 1.961

7.  The need for a new medical model: a challenge for biomedicine.

Authors:  G L Engel
Journal:  Science       Date:  1977-04-08       Impact factor: 47.728

8.  Graphical Data Analysis on the Circle: Wrap-Around Time Series Plots for (Interrupted) Time Series Designs.

Authors:  Joseph Lee Rodgers; William Howard Beasley; Matthew Schuelke
Journal:  Multivariate Behav Res       Date:  2014 Nov-Dec       Impact factor: 5.923

Review 9.  The biopsychosocial approach to chronic pain: scientific advances and future directions.

Authors:  Robert J Gatchel; Yuan Bo Peng; Madelon L Peters; Perry N Fuchs; Dennis C Turk
Journal:  Psychol Bull       Date:  2007-07       Impact factor: 17.737

10.  Let me Google that for you: a time series analysis of seasonality in internet search trends for terms related to foot and ankle pain.

Authors:  Scott Telfer; James Woodburn
Journal:  J Foot Ankle Res       Date:  2015-07-03       Impact factor: 2.303

View more
  2 in total

1.  "Dr. Google, I am in Pain"-Global Internet Searches Associated with Pain: A Retrospective Analysis of Google Trends Data.

Authors:  Mikołaj Kamiński; Igor Łoniewski; Wojciech Marlicz
Journal:  Int J Environ Res Public Health       Date:  2020-02-04       Impact factor: 3.390

2.  Lifestyle Disease Surveillance Using Population Search Behavior: Feasibility Study.

Authors:  Shahan Ali Memon; Saquib Razak; Ingmar Weber
Journal:  J Med Internet Res       Date:  2020-01-27       Impact factor: 5.428

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.