The Long COVID experience from a patient's perspective: a clustering analysis of 27,216 Reddit posts

ObjectiveThis work aims to study the profiles of Long COVID from the perspective of the patients spontaneously sharing their experiences and symptoms on Reddit.MethodsWe collected 27,216 posts shared between July 2020 and July 2022 on Long COVID-related Reddit forums. Natural language processing, clustering techniques and a Long COVID symptoms lexicon were used to extract the different symptoms and categories of symptoms and to study the co-occurrences and correlation between them.ResultsMore than 78% of the posts mentioned at least one Long COVID symptom. Fatigue (29.4%), pain (22%), clouded consciousness (19.1%), anxiety (17.7%) and headaches (15.6%) were the most prevalent symptoms. They also highly co-occurred with a variety of other symptoms (e.g., fever, sinonasal congestion). Different categories of symptoms were found: general (45.5%), neurological/ocular (42.9%), mental health/psychological/behavioral (35.2%), body pain/mobility (35.1%) and cardiorespiratory (31.2%). Posts focusing on other concerns of the community such as vaccine, recovery and relapse and, symptom triggers were detected.ConclusionsWe demonstrated the benefits of leveraging large volumes of data from Reddit to characterize the heterogeneity of Long COVID profiles. General symptoms, particularly fatigue, have been reported to be the most prevalent and frequently co-occurred with other symptoms. Other concerns, such as vaccination and relapse following recovery, were also addressed by the Long COVID community

The Use of Social Media for Health Research Purposes: Scoping Review.

peer reviewed[en] BACKGROUND: As social media are increasingly used worldwide, more and more scientists are relying on them for their health-related projects. However, social media features, methodologies, and ethical issues are unclear so far because, to our knowledge, there has been no overview of this relatively young field of research. OBJECTIVE: This scoping review aimed to provide an evidence map of the different uses of social media for health research purposes, their fields of application, and their analysis methods. METHODS: We followed the scoping review methodologies developed by Arksey and O'Malley and the Joanna Briggs Institute. After developing search strategies based on keywords (eg, social media, health research), comprehensive searches were conducted in the PubMed/MEDLINE and Web of Science databases. We limited the search strategies to documents written in English and published between January 1, 2005, and April 9, 2020. After removing duplicates, articles were screened at the title and abstract level and at the full text level by two independent reviewers. One reviewer extracted data, which were descriptively analyzed to map the available evidence. RESULTS: After screening 1237 titles and abstracts and 407 full texts, 268 unique papers were included, dating from 2009 to 2020 with an average annual growth rate of 32.71% for the 2009-2019 period. Studies mainly came from the Americas (173/268, 64.6%, including 151 from the United States). Articles used machine learning or data mining techniques (60/268) to analyze the data, discussed opportunities and limitations of the use of social media for research (59/268), assessed the feasibility of recruitment strategies (45/268), or discussed ethical issues (16/268). Communicable (eg, influenza, 40/268) and then chronic (eg, cancer, 24/268) diseases were the two main areas of interest. CONCLUSIONS: Since their early days, social media have been recognized as resources with high potential for health research purposes, yet the field is still suffering from strong heterogeneity in the methodologies used, which prevents the research from being compared and generalized. For the field to be fully recognized as a valid, complementary approach to more traditional health research study designs, there is now a need for more guidance by types of applications of social media for health research, both from a methodological and an ethical perspective. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2020-040671

Global diabetes burden: analysis of regional differences to improve diabetes care.

peer reviewed[en] INTRODUCTION: The current evaluation processes of the burden of diabetes are incomplete and subject to bias. This study aimed to identify regional differences in the diabetes burden on a universal level from the perspective of people with diabetes. RESEARCH DESIGN AND METHODS: We developed a worldwide online diabetes observatory based on 34 million diabetes-related tweets from 172 countries covering 41 languages, spanning from 2017 to 2021. After translating all tweets to English, we used machine learning algorithms to remove institutional tweets and jokes, geolocate users, identify topics of interest and quantify associated sentiments and emotions across the seven World Bank regions. RESULTS: We identified four topics of interest for people with diabetes (PWD) in the Middle East and North Africa and another 18 topics in North America. Topics related to glycemic control and food are shared among six regions of the world. These topics were mainly associated with sadness (35% and 39% on average compared with levels of sadness in other topics). We also revealed several region-specific concerns (eg, insulin pricing in North America or the burden of daily diabetes management in Europe and Central Asia). CONCLUSIONS: The needs and concerns of PWD vary significantly worldwide, and the burden of diabetes is perceived differently. Our results will support better integration of these regional differences into diabetes programs to improve patient-centric diabetes research and care, focused on the most relevant concerns to enhance personalized medicine and self-management of PWD

Use of artificial intelligence methods for the analysis of real-world and social media data in digital epidemiology

Introduction Among all the digital data sources, social media have emerged as a significant source of health-related information, offering access to patient perspectives, outcomes and experiences. This rapid access to patients’ emotions and concerns at a large scale represents a unique opportunity to improve patient-centered research and care. Relying on its dynamic online community, this thesis will focus on people living with diabetes, with an aim to better describe and understand the burden of diabetes. As part of the World Diabetes Distress Study, this thesis explores the potential of using social media data for health research and digital epidemiology using artificial intelligence methods for chronic diseases to go beyond the historical analysis of online data to monitor infectious disease epidemics. The overall aim is to demonstrate how social media data can capture key insights from health-related discussions and shape and enhance healthcare strategies. Methods We first used a scoping review approach to identify all the different uses of social media for health research purposes. Second, a global analysis of diabetes-related tweets was conducted to identify the critical determinants of diabetes burden and the differences in how diabetes is perceived worldwide. Then, we developed the concept of a virtual digital cohort study (VDCS) and designed a specialized tool to standardize and analyze social media data as a typical cohort study. Results We have shown that social media platforms can be used for health research. It can be used for various tasks, from recruitment to the dissemination of information and data collection. The rich information shared by the communities of people with diabetes can be used as a complementary approach to traditional, questionnaire-based epidemiology. This project led to the analysis of 54 million diabetes-related tweets collected between 2017 and 2021, thereby enhancing our understanding of the diabetes burden worldwide. An open-source Python package ALTRUIST was created to standardize and simplify setting up VDCS using social media data. It allows researchers to effectively navigate through various stages of data collection, pre-processing, and analysis, simulating a traditional cohort study using social media data. Discussion/Conclusion This research highlights the potential of social media data in health research and digital epidemiology. Social media data can give a valuable, unbiased, unrestricted and unfiltered insight into patients' daily lives and experiences complementary to traditional approaches. The ALTRUIST package was designed to standardize the analysis of such data like a cohort and to help the research community to develop social-media-based research projects. Significant ethical, methodological and technical challenges remain to be addressed as we continue to deepen the field. Therefore, standardization of methodologies is necessary to gain the impact of results and trust from healthcare professionals. This work can be considered a first step towards a cohesive, standardized field to boost patient-centered care and global health strategies

Scoping review protocol on the use of social media for health research purposes

Introduction More than one-third of the world population uses at least one form of social media. Since their advent in 2005, health-oriented research based on social media data has largely increased as discussions about health issues are broadly shared online and generate a large amount of health-related data. The objective of this scoping review is to provide an evidence map of the various uses of social media for health research purposes, their fields of applications and their analysis methods.Methods and analysis This scoping review will follow the Arksey and O’Malley methodological framework (2005) as well as the Joanna Briggs Institute Reviewer’s manual. Relevant publications will be first searched on the PudMed/MEDLINE database and then on Web of Science. We will focus on literature published between January 2005 and April 2020. All articles related to the use of social media or networks for health-oriented research purposes will be included. A first search will be conducted with some keywords in order to identify relevant articles. After identifying the research strategy, a two-part study selection process will be systematically applied by two reviewers. The first part consists of screening titles and abstracts found, thanks to the search strategy, to define the eligibility of each article. In the second part, the full texts will be screened and only relevant articles will be kept. Data will finally be extracted, collated and charted to summarise all the relevant methods, outcomes and key findings in the articles.Ethics and dissemination This scoping review will provide an extensive overview of the use of social media for health research purposes. Opportunities as well as future ethical, methodological and technical challenges will also be discussed based on our findings to define a new research agenda. Results will be disseminated through a peer-reviewed publication

Table_3_The Long COVID experience from a patient's perspective: a clustering analysis of 27,216 Reddit posts.XLSX

ObjectiveThis work aims to study the profiles of Long COVID from the perspective of the patients spontaneously sharing their experiences and symptoms on Reddit.MethodsWe collected 27,216 posts shared between July 2020 and July 2022 on Long COVID-related Reddit forums. Natural language processing, clustering techniques and a Long COVID symptoms lexicon were used to extract the different symptoms and categories of symptoms and to study the co-occurrences and correlation between them.ResultsMore than 78% of the posts mentioned at least one Long COVID symptom. Fatigue (29.4%), pain (22%), clouded consciousness (19.1%), anxiety (17.7%) and headaches (15.6%) were the most prevalent symptoms. They also highly co-occurred with a variety of other symptoms (e.g., fever, sinonasal congestion). Different categories of symptoms were found: general (45.5%), neurological/ocular (42.9%), mental health/psychological/behavioral (35.2%), body pain/mobility (35.1%) and cardiorespiratory (31.2%). Posts focusing on other concerns of the community such as vaccine, recovery and relapse and, symptom triggers were detected.ConclusionsWe demonstrated the benefits of leveraging large volumes of data from Reddit to characterize the heterogeneity of Long COVID profiles. General symptoms, particularly fatigue, have been reported to be the most prevalent and frequently co-occurred with other symptoms. Other concerns, such as vaccination and relapse following recovery, were also addressed by the Long COVID community.</p

Image_1_The Long COVID experience from a patient's perspective: a clustering analysis of 27,216 Reddit posts.PDF

ObjectiveThis work aims to study the profiles of Long COVID from the perspective of the patients spontaneously sharing their experiences and symptoms on Reddit.MethodsWe collected 27,216 posts shared between July 2020 and July 2022 on Long COVID-related Reddit forums. Natural language processing, clustering techniques and a Long COVID symptoms lexicon were used to extract the different symptoms and categories of symptoms and to study the co-occurrences and correlation between them.ResultsMore than 78% of the posts mentioned at least one Long COVID symptom. Fatigue (29.4%), pain (22%), clouded consciousness (19.1%), anxiety (17.7%) and headaches (15.6%) were the most prevalent symptoms. They also highly co-occurred with a variety of other symptoms (e.g., fever, sinonasal congestion). Different categories of symptoms were found: general (45.5%), neurological/ocular (42.9%), mental health/psychological/behavioral (35.2%), body pain/mobility (35.1%) and cardiorespiratory (31.2%). Posts focusing on other concerns of the community such as vaccine, recovery and relapse and, symptom triggers were detected.ConclusionsWe demonstrated the benefits of leveraging large volumes of data from Reddit to characterize the heterogeneity of Long COVID profiles. General symptoms, particularly fatigue, have been reported to be the most prevalent and frequently co-occurred with other symptoms. Other concerns, such as vaccination and relapse following recovery, were also addressed by the Long COVID community.</p

Table_1_The Long COVID experience from a patient's perspective: a clustering analysis of 27,216 Reddit posts.XLSX

ObjectiveThis work aims to study the profiles of Long COVID from the perspective of the patients spontaneously sharing their experiences and symptoms on Reddit.MethodsWe collected 27,216 posts shared between July 2020 and July 2022 on Long COVID-related Reddit forums. Natural language processing, clustering techniques and a Long COVID symptoms lexicon were used to extract the different symptoms and categories of symptoms and to study the co-occurrences and correlation between them.ResultsMore than 78% of the posts mentioned at least one Long COVID symptom. Fatigue (29.4%), pain (22%), clouded consciousness (19.1%), anxiety (17.7%) and headaches (15.6%) were the most prevalent symptoms. They also highly co-occurred with a variety of other symptoms (e.g., fever, sinonasal congestion). Different categories of symptoms were found: general (45.5%), neurological/ocular (42.9%), mental health/psychological/behavioral (35.2%), body pain/mobility (35.1%) and cardiorespiratory (31.2%). Posts focusing on other concerns of the community such as vaccine, recovery and relapse and, symptom triggers were detected.ConclusionsWe demonstrated the benefits of leveraging large volumes of data from Reddit to characterize the heterogeneity of Long COVID profiles. General symptoms, particularly fatigue, have been reported to be the most prevalent and frequently co-occurred with other symptoms. Other concerns, such as vaccination and relapse following recovery, were also addressed by the Long COVID community.</p

Table_4_The Long COVID experience from a patient's perspective: a clustering analysis of 27,216 Reddit posts.XLSX

ObjectiveThis work aims to study the profiles of Long COVID from the perspective of the patients spontaneously sharing their experiences and symptoms on Reddit.MethodsWe collected 27,216 posts shared between July 2020 and July 2022 on Long COVID-related Reddit forums. Natural language processing, clustering techniques and a Long COVID symptoms lexicon were used to extract the different symptoms and categories of symptoms and to study the co-occurrences and correlation between them.ResultsMore than 78% of the posts mentioned at least one Long COVID symptom. Fatigue (29.4%), pain (22%), clouded consciousness (19.1%), anxiety (17.7%) and headaches (15.6%) were the most prevalent symptoms. They also highly co-occurred with a variety of other symptoms (e.g., fever, sinonasal congestion). Different categories of symptoms were found: general (45.5%), neurological/ocular (42.9%), mental health/psychological/behavioral (35.2%), body pain/mobility (35.1%) and cardiorespiratory (31.2%). Posts focusing on other concerns of the community such as vaccine, recovery and relapse and, symptom triggers were detected.ConclusionsWe demonstrated the benefits of leveraging large volumes of data from Reddit to characterize the heterogeneity of Long COVID profiles. General symptoms, particularly fatigue, have been reported to be the most prevalent and frequently co-occurred with other symptoms. Other concerns, such as vaccination and relapse following recovery, were also addressed by the Long COVID community.</p

Table_2_The Long COVID experience from a patient's perspective: a clustering analysis of 27,216 Reddit posts.XLSX

ObjectiveThis work aims to study the profiles of Long COVID from the perspective of the patients spontaneously sharing their experiences and symptoms on Reddit.MethodsWe collected 27,216 posts shared between July 2020 and July 2022 on Long COVID-related Reddit forums. Natural language processing, clustering techniques and a Long COVID symptoms lexicon were used to extract the different symptoms and categories of symptoms and to study the co-occurrences and correlation between them.ResultsMore than 78% of the posts mentioned at least one Long COVID symptom. Fatigue (29.4%), pain (22%), clouded consciousness (19.1%), anxiety (17.7%) and headaches (15.6%) were the most prevalent symptoms. They also highly co-occurred with a variety of other symptoms (e.g., fever, sinonasal congestion). Different categories of symptoms were found: general (45.5%), neurological/ocular (42.9%), mental health/psychological/behavioral (35.2%), body pain/mobility (35.1%) and cardiorespiratory (31.2%). Posts focusing on other concerns of the community such as vaccine, recovery and relapse and, symptom triggers were detected.ConclusionsWe demonstrated the benefits of leveraging large volumes of data from Reddit to characterize the heterogeneity of Long COVID profiles. General symptoms, particularly fatigue, have been reported to be the most prevalent and frequently co-occurred with other symptoms. Other concerns, such as vaccination and relapse following recovery, were also addressed by the Long COVID community.</p