Open Research Newcastle
Browse

Tweeting back: predicting new cases of back pain with mass social media data

Download (785.09 kB)
journal contribution
posted on 2025-05-10, 12:53 authored by Hopin Lee, James H. McAuley, Markus Hübscher, Heidi G. Allen, Steven J. Kamper, G. Lorimer Moseley
Background: Back pain is a global health problem. Recent research has shown that risk factors that are proximal to the onset of back pain might be important targets for preventive interventions. Rapid communication through social media might be useful for delivering timely interventions that target proximal risk factors. Identifying individuals who are likely to discuss back pain on Twitter could provide useful information to guide on- line interventions. Methods: We used a case-crossover study design for a sample of 742 028 tweets about back pain to quantify the risks associated with a new tweet about back pain. Results: The odds of tweeting about back pain just after tweeting about selected physical, psychological, and general health factors were 1.83 (95% confidence interval [CI], 1.80-1.85), 1.85 (95% CI: 1.83-1.88), and 1.29 (95% CI, 1.27-1.30), respectively. Conclusion: These findings give directions for future research that could use social media for innovative public health interventions.

History

Journal title

Journal of the American Medical Informatics Association

Volume

23

Issue

3

Pagination

644-648

Publisher

Oxford University Press

Language

  • en, English

College/Research Centre

Faculty of Health and Medicine

School

School of Medicine and Public Health

Rights statement

This is a pre-copyedited, author-produced version of an article accepted for publication Journal of the American Medical Informatics Association following peer review. The version of record Lee, Hopin; McAuley, James H.; Hübscher, Markus; Allen, Heidi G.; Kamper, Steven J.; Moseley, G. Lorimer “Tweeting back: predicting new cases of back pain with mass social media data”, Published in the Journal of the American Medical Informatics Association Vol. 23, Issue 3, p. 644-648. (2016) is available online at: http://dx.doi.org/10.1093/jamia/ocv168 Accessed from: http://hdl.handle.net/1959.13/1338789

Usage metrics

    Publications

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC