The Impact of the COVID-19 Pandemic on People with Rheumatic and Musculoskeletal Diseases: Insights from Patient-Generated Data on Social Media
Oxford University Press
During the COVID-19 pandemic, much communication occurred online, through social media. This study aimed to provide patient perspective data on how the COVID-19 pandemic impacted people with rheumatic and musculoskeletal diseases (RMDs), using Twitter-based patient-generated health data (PGHD).
A convenience sample of Twitter messages in English posted by people with RMDs was extracted between 1 March and 12 July 2020 and examined using thematic analysis. Included were Twitter messages that mentioned keywords and hashtags related to both COVID-19 (or SARS-CoV-2) and select RMDs. The RMDs monitored included inflammatory-driven (joint) conditions (ankylosing spondylitis, RA, PsA, lupus/SLE and gout).
The analysis included 569 tweets by 375 Twitter users with RMDs across several countries. Eight themes emerged regarding the impact of the COVID-19 pandemic on people with RMDs: (i) lack of understanding of SARS-CoV-2/COVID-19; (ii) critical changes in health behaviour; (iii) challenges in healthcare practice and communication with healthcare professionals; (iv) difficulties with access to medical care; (v) negative impact on physical and mental health, coping strategies; (vi) issues around work participation; (vii) negative effects of the media; and (viii) awareness-raising.
The findings show that Twitter serves as a real-time data source to understand the impact of the COVID-19 pandemic on people with RMDs. The platform provided ‘early signals’ of potentially critical health behaviour changes. Future epidemics might benefit from the real-time use of Twitter-based PGHD to identify emerging health needs, facilitate communication and inform clinical practice decisions.
Reuter, Katja; Deodhar, Atul; Makri, Souzi; Zimmer, Michael; Berenbaum, Francis; and Nikiphorou, Elena, "The Impact of the COVID-19 Pandemic on People with Rheumatic and Musculoskeletal Diseases: Insights from Patient-Generated Data on Social Media" (2021). Computer Science Faculty Research and Publications. 55.
ADA Accessible Version
Accepted version. Rheumatology, Vol. 60, No. S1 (October 2021): S177-S184. DOI. © 2021 Oxford University Press. Used with permission.