ArticleJournal of medical Internet research2016
Patterns of Treatment Switching in Multiple Sclerosis Therapies in US Patients Active on Social Media: Application of Social Media Content Analysis to Health Outcomes Research.
Article in Journal of medical Internet research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 40 citations in OpenAlex.
- Long-Term Management and Therapeutic Sequencing for Patients with Relapsing Multiple Sclerosis in France: A Vignette Study.Neurology and therapy · 2025Article
- Best practices for the collection and analysis of patient experience data from social media for patient-focused drug development.Frontiers in medicine · 2025Review
- Using Social Media as a Source of Real-World Data for Pharmaceutical Drug Development and Regulatory Decision Making.Drug safety · 2024Article
- Patient Experiences and Insights on Chronic Ocular Pain: Social Media Listening Study.JMIR formative research · 2024Article
- Artificial intelligence-enabled social media listening to inform early patient-focused drug development: perspectives on approaches and strategies.Frontiers in digital health · 2024Article
- Understanding the Lived Experiences of Patients With Melanoma: Real-World Evidence Generated Through a European Social Media Listening Analysis.JMIR cancer · 2022Article
- Using Infodemiology Metrics to Assess Public Interest in Liver Transplantation: Google Trends Analysis.Journal of medical Internet research · 2021Article
- Findings in Chinese Patients With Parkinson's Disease: A Content Analysis From the SML Study.Frontiers in psychiatry · 2021Article
- Spontaneously Generated Online Patient Experience of Modafinil: A Qualitative and NLP Analysis.Frontiers in digital health · 2021Article
- Infodemiology and Infoveillance: Scoping Review.Journal of medical Internet research · 2020Article
- Preface of Special Issue "Cares in the Age of Communication: Health Education and Healthy Lifestyles": Social Media and Health Communication in a Pandemic?European journal of investigation in health, psychology and education · 2020Article
- Validity of social media for assessing treatment patterns in oncology patients: a case study in melanoma.JAMIA open · 2019Article
- Heartburn-Related Internet Searches and Trends of Interest across Six Western Countries: A Four-Year Retrospective Analysis Using Google Ads Keyword Planner.International journal of environmental research and public health · 2019Article
- Evaluating Patient Experiences in Dry Eye Disease Through Social Media Listening Research.Ophthalmology and therapy · 2019Article
- Patient-Level Prediction of Cardio-Cerebrovascular Events in Hypertension Using Nationwide Claims Data.Journal of medical Internet research · 2019Article
- Real-world data and the patient perspective: the PROmise of social media?BMC medicine · 2019Article
- Use of Social Media in the Assessment of Relative Effectiveness: Explorative Review With Examples From Oncology.JMIR cancer · 2018Article
- Perspectives from the Patient and the Healthcare Professional in Multiple Sclerosis: Social Media and Patient Education.Neurology and therapy · 2018Article
- Social Media and Multiple Sclerosis in the Posttruth Age.Interactive journal of medical research · 2017Article
- Linked Patient-Reported Outcomes Data From Patients With Multiple Sclerosis Recruited on an Open Internet Platform to Health Care Claims Databases Identifies a Representative Population for Real-Life Data Analysis in Multiple Sclerosis.Journal of medical Internet research · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSocial media analysis has rarely been applied to the study of specific questions in outcomes research.
objectiveThe aim was to test the applicability of social media analysis to outcomes research using automated listening combined with filtering and analysis of data by specialists. After validation, the process was applied to the study of patterns of treatment switching in multiple sclerosis (MS).
methodsA comprehensive listening and analysis process was developed that blended automated listening with filtering and analysis of data by life sciences-qualified analysts and physicians. The population was patients with MS from the United States. Data sources were Facebook, Twitter, blogs, and online forums. Sources were searched for mention of specific oral, injectable, and intravenous (IV) infusion treatments. The representativeness of the social media population was validated by comparison with community survey data and with data from three large US administrative claims databases: MarketScan, PharMetrics Plus, and Department of Defense.
resultsA total of 10,260 data points were sampled for manual review: 3025 from Twitter, 3771 from Facebook, 2773 from Internet forums, and 691 from blogs. The demographics of the social media population were similar to those reported from community surveys and claims databases. Mean age was 39 (SD 11) years and 14.56% (326/2239) of the population was older than 50 years. Women, patients aged 30 to 49 years, and those diagnosed for more than 10 years were represented by more data points than other patients were. Women also accounted for a large majority (82.6%, 819/991) of reported switches. Two-fifths of switching patients had lived with their disease for more than 10 years since diagnosis. Most reported switches (55.05%, 927/1684) were from injectable to oral drugs with switches from IV therapies to orals the second largest switch (15.38%, 259/1684). Switches to oral drugs accounted for more than 80% (927/1114) of the switches away from injectable therapies. Four reasons accounted for more than 90% of all switches: severe side effects, lack of efficacy, physicians' advice, and greater ease of use. Side effects were the main reason for switches to oral or to injectable therapies and search for greater efficacy was the most important factor in switches to IV therapies. Cost of medication was the reason for switching in less than 0.5% of patients.
conclusionsSocial intelligence can be applied to outcomes research with power to analyze MS patients' personal experiences of treatments and to chart the most common reasons for switching between therapies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.