key: cord-0752494-2ritq33w
authors: Reuter, Katja; Deodhar, Atul; Makri, Souzi; Zimmer, Michael; Berenbaum, Francis; Nikiphorou, Elena
title: COVID-19 pandemic impact on people with rheumatic and musculoskeletal diseases: Insights from patient-generated health data on social media
date: 2021-02-26
journal: Rheumatology (Oxford)
DOI: 10.1093/rheumatology/keab174
sha: 636758f32cc161dff339dd01072f52b9e3a39229
doc_id: 752494
cord_uid: 2ritq33w

OBJECTIVES: 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). METHODS: A convenience sample of Twitter messages in English posted by people with RMDs was extracted between March 1, and July 12, 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, Rheumatoid Arthritis, Psoriatic Arthritis, Lupus/Systemic Lupus Erythematosus, and Gout). RESULTS: 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: (1) lack of understanding of SARS-CoV-2/COVID-19; (2) critical changes in health behaviour; (3) challenges in healthcare practice and communication with healthcare professionals; (4) difficulties with access to medical care; (5) negative impact on physical and mental health, coping strategies; (6) issues around work participation, (7) negative effects of the media; (8) awareness-raising. CONCLUSION: 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.

The coronavirus disease 2019 (COVID-19) pandemic [1] and resulting safety measures have impacted people's lives in multiple ways, including their medical care access.

People had to adjust to social distancing and self-quarantine rules, lockdowns and movement restrictions, wearing masks, and a rapid transition to telework and telehealth approaches. Whether people with rheumatic and musculoskeletal diseases (RMDs) are at an increased risk for poorer outcomes is currently debated [2] .

This study provides a thematic analysis of perspectives about the impact of the COVID-19 pandemic among people with RMDs. We used patient-generated health data (PGHD) from the social network Twitter. PGHD is defined as "health-related data created, gathered, or inferred by or from patients and for which the patient controls data collection and data sharing" [3] .

With the restrictions on public life, much information seeking and conversation about the pandemic occurred online, including social media (SM). Public opinions expressed on SM provide valuable insight to better understand the dynamics of the COVID -19 pandemic. Research has demonstrated the usefulness of Twitter user data to understand public perspectives on various diseases and health topics [4, 5] .

A thematic analysis was conducted, including public Twitter messages (tweets) in English that mentioned keywords and hashtags related to both Covid-19 (or SARS-CoV-2) and RMDs (Table 1 ) and were posted between February 1, 2020, and July 14, 2020. A hashtag is a word or phrase preceded by a hash sign (#) and used to identify Twitter messages on a specific topic (e.g., #arthritis). A non-probability, convenience sampling method that relied on the manual monitoring of posts using Twitter's public search interface was used. The hashtags used in the search were selected based on previous research [6] Included in this study were original messages by people with RMDs (regardless of their location). The program BotOrNot [8] was used to analyse the Twitter user handles (e.g., @JohnSmith) and distinguish human users from automated accounts. Due to this study's focus on original patient perspectives, we excluded the following types of tweets from the analysis dataset: (i) fake, commercial, and bot-like accounts, (ii) retweets, (iii) non-English Twitter posts, and (iv) posts from Twitter users not clearly stating in their Twitter profile description or their tweet(s) that they have an RMD.

Two authors (KR, EN) independently reviewed the tweets to classify the content based on a priori and emergent coding categories ( Table 2 , column 1). The coding categories were developed, incorporating COVID-related research [9] [10] and input from rheumatology experts. The authors (KR and EN) discussed their coding decisions and any discrepancies until they reached consensus.

This study relied on publicly available Twitter data. The authors adhered to Twitter's terms and conditions, terms of use, and privacy policy. The Institutional Review Board at King's College London confirmed that no additional institutional ethical approval was necessary to analyse public tweets. We received consent via Twitter from the Twitter users whose verbatim tweets are included in this manuscript.

A patient research partner (SM) was involved in the study's design and conduct, including the outreach and consent messages and interpretation of the data.

The analysis included 569 tweets published by 375 Twitter users that we identified as people with RMDs in several countries (e.g., UK, Portugal, USA, Taiwan, Australia, Canada). In addition to previously existing hashtags (e.g., #arthritis, #lupuswarrior), the sampled population used newly emerging user-generated hashtags related to COVID-19 in their messages, such as #coronavirusUK, #HighRiskCovid19, #COVIDarthritis, #covidsurvivor, #SocialDistancing, #SelfIsolation, #QuarantineLife, #WithoutMyHCQ, #covidtest, #TreatmentRationing #RationingOfCare, #WearAMask. We did not identify relevant tweets related to gout. Table S1 .

This study is the first to our knowledge to use and demonstrate the value of Twitter as a real-time data source to understand the impact of the COVID-19 pandemic on people with RMDs. Despite the hundreds of scientific manuscripts that have emerged since the onset of the pandemic focusing on quantitative research and specific patient and disease-related associations with COVID-19 outcomes, studies exploring the direct patient perspective have been scarce. The COVID-19 outbreak has placed people with RMDs at the centre of this pandemic, due to the multiple uncertainties, including medication shortages [11] and their underlying, chronic, autoimmune disease and their treatment needs. Attempts to date to identify how the pandemic affects these individuals have been based primarily on quantitative research but these studies lack the direct patient perspective. Compared to traditional methods to obtain patient perspective data (e.g. surveys/telephone interviews), researchers have described the benefits of SMbased patient-generated health data as "unprimed by researchers," and "without instrument bias" [12] . SM has been proposed as an essential communications tool in global health crises, including the current COVID-19 pandemic [13] . Opinions expressed by RMD patients on SM can provide valuable insights to better understand the COVID-19 pandemic dynamics, for example, gauging their attitudes towards safety measures and mapping physical and mental health symptoms.

In our study, the data provided 'early signals' of health behaviour changes that could potentially adversely affect people's health outcomes (e.g., medication rationing, missing face-to-face lab monitoring appointments). Similar behaviours have been seen in other pandemics such as the 2015 Middle East Respiratory Syndrome (MERS) outbreak in South Korea, where people avoided hospitals even when sick [14] .

Additionally, our study detected 'signs' of increased anxiety and depression levels among people with RMDs, as reported previously [15] . The presence of patient 'signals' provides opportunities for future pandemics. Automated SM surveillance efforts through the rapid analysis of vast amounts of text (i.e., natural language processing) [16] and monitoring for keywords that indicate health-harming health behaviours could help identify individuals for targeted health promotion interventions during a pandemic. Data from a recent survey study suggests that SM has a "positive influence on public health protection" against the COVID-19 pandemic with public health awareness and public health behavioural changes acting as "partial mediators" [17] . However, the effects of SM interventions on public health protection against a pandemic are still poorly understood.

Our study highlights an unmet need: to provide clear and consistent information and communication between healthcare professionals and RMD patients. Future research could explore to what extent Twitter might serve as a platform to bridge the communication gap with RMD patients through different health promotion interventions.

Research has shown that pandemics call for unique health communication and education strategies in which public health agencies need to satisfy the public's information needs about possible risks while preventing risk exaggeration and dramatization [18] . More proactive health promotion activities on SM to inform about the pandemic and safety measures are also highly relevant in light of the COVID-19 'infodemic', i.e., the overabundance of both accurate and inaccurate information that occurs during an epidemic [19] . Platforms such as Twitter are particularly relevant in pandemics since they can provide a dynamic reflection of the impact of a pandemic. Despite these limitations, our study has several strengths, including its use of direct patient perspective data on a worldwide pandemic, highlighting a range of issues concerning physical and psychological health, access to medication and medical care, work participation, and other stressors. The data provide a better understanding of how safety measures and medical care could be communicated and delivered in the future, e.g., through regular webinars by health professionals that address the emerging questions and concerns of people with RMDs during a pandemic. We advocate using patient-generated health data from the social network Twitter as a knowledge source, similar to focus groups, to help shape the response of health care professionals and government authorities to a pandemic.

We thank the Twitter users who allowed us to include their public tweets as examples in this article. We were overwhelmed by their enthusiasm for this research and positive feedback. Their tweet contributions make this report much more tangible, showing firsthand how they experienced the COVID-19 pandemic. The following individuals have agreed to be identified directly as contributing to this paper (listed alphabetically): @annakatarinaz; Anna @skeptikosbel; Carmen S. Heath; ChronicallySingle; David Meldrum @vicardave; Debbie Fenelon, Don @drfrasher; Elisa Comer; @Eviljohna; Fiona Grant; Jackie Dillon, Jennifer @UnxpctdAdvocate; @joelvsarthritis; Judy Nagy; Kate Betteridge; @kimberleyscribe; Marcie Rhysling; Martin Hale; Michael Kuluva @MichaelKuluva; Miss D @Miss_Danielle82; Molly Schreiber; @NessMonet; @Phant0mGam3s; Platypus Custard; @PurpleGimp; Randi Vinton-Stewart @randistewart80; @SpoonieMomBlog; Thérèse @TerezHumphrey; Tinu Abayomi-Paul @Tinu; @twility; and Wendy Hunter. Thank you for supporting this research.

No specific funding was received from any bodies in the public, commercial or not-forprofit sectors to carry out the work described in this article.

The authors declare no conflicts of interest.

This study relied on publicly available Twitter data. The authors adhered to Twitter's terms and conditions, terms of use, and privacy policy. The Institutional Review Board at King's College London confirmed that no additional institutional ethical approval was necessary to analyse public tweets. We received consent via Twitter from the Twitter users whose verbatim tweets are included in this manuscript.

The study data will be available upon request due to the fact that it includes original Twitter messages and user accounts that can be identified. Healthcare practice Regarding medical care, patients commented on healthcare staff's professional behaviour, expressing confidence in front-line workers, e.g., nurses, "as if nothing phased them." Still, they also voiced concern about "non-staff" not adhering to safety measures, e.g., wearing masks in the hospital. 

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