- Title
- Tweeting back: predicting new cases of back pain with mass social media data
- Creator
- Lee, Hopin; McAuley, James H.; Hübscher, Markus; Allen, Heidi G.; Kamper, Steven J.; Moseley, G. Lorimer
- Relation
- Journal of the American Medical Informatics Association Vol. 23, Issue 3, p. 644-648
- Publisher Link
- http://dx.doi.org/10.1093/jamia/ocv168
- Publisher
- Oxford University Press
- Resource Type
- journal article
- Date
- 2016
- Description
- 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.
- Subject
- back pain; Twitter; public health; social media; case-crossover
- Identifier
- http://hdl.handle.net/1959.13/1338789
- Identifier
- uon:28099
- Identifier
- ISSN:1067-5027
- Rights
- 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
- Language
- eng
- Full Text
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