Tweeting back: predicting new cases of back pain with mass social media data. (11th December 2015)

• Record Type: Journal Article
• Authors: Lee, Hopin; McAuley, James H; Hübscher, Markus; Allen, Heidi G; Kamper, Steven J; Moseley, G Lorimer
• Is Part Of: Journal of the American Medical Informatics Association. Volume 23:Number 3(2016:May)
• Page Start: 644
• Page End: 648
• Publication Date: 2015-12-11
• Subjects: back pain -- Twitter -- public health -- social media -- case-crossover; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocv168
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract : 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 online 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.