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Crowdsourced Health Research Studies: An Important Emerging Complement To Clinical Trials In The Public Health Research Ecosystem

M. Swan
Published 2012 · Medicine

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BACKGROUND Crowdsourced health research studies are the nexus of three contemporary trends: 1) citizen science (non-professionally trained individuals conducting science-related activities); 2) crowdsourcing (use of web-based technologies to recruit project participants); and 3) medicine 2.0 / health 2.0 (active participation of individuals in their health care particularly using web 2.0 technologies). Crowdsourced health research studies have arisen as a natural extension of the activities of health social networks (online health interest communities), and can be researcher-organized or participant-organized. In the last few years, professional researchers have been crowdsourcing cohorts from health social networks for the conduct of traditional studies. Participants have also begun to organize their own research studies through health social networks and health collaboration communities created especially for the purpose of self-experimentation and the investigation of health-related concerns. OBJECTIVE The objective of this analysis is to undertake a comprehensive narrative review of crowdsourced health research studies. This review will assess the status, impact, and prospects of crowdsourced health research studies. METHODS Crowdsourced health research studies were identified through a search of literature published from 2000 to 2011 and informal interviews conducted 2008-2011. Keyword terms related to crowdsourcing were sought in Medline/PubMed. Papers that presented results from human health studies that included crowdsourced populations were selected for inclusion. Crowdsourced health research studies not published in the scientific literature were identified by attending industry conferences and events, interviewing attendees, and reviewing related websites. RESULTS Participatory health is a growing area with individuals using health social networks, crowdsourced studies, smartphone health applications, and personal health records to achieve positive outcomes for a variety of health conditions. PatientsLikeMe and 23andMe are the leading operators of researcher-organized, crowdsourced health research studies. These operators have published findings in the areas of disease research, drug response, user experience in crowdsourced studies, and genetic association. Quantified Self, Genomera, and DIYgenomics are communities of participant-organized health research studies where individuals conduct self-experimentation and group studies. Crowdsourced health research studies have a diversity of intended outcomes and levels of scientific rigor. CONCLUSIONS Participatory health initiatives are becoming part of the public health ecosystem and their rapid growth is facilitated by Internet and social networking influences. Large-scale parameter-stratified cohorts have potential to facilitate a next-generation understanding of disease and drug response. Not only is the large size of crowdsourced cohorts an asset to medical discovery, too is the near-immediate speed at which medical findings might be tested and applied. Participatory health initiatives are expanding the scope of medicine from a traditional focus on disease cure to a personalized preventive approach. Crowdsourced health research studies are a promising complement and extension to traditional clinical trials as a model for the conduct of health research.
This paper references
10.1097/GIM.0b013e3181d5f73b
Multigenic condition risk assessment in direct-to-consumer genomic services
Melanie Swan (2010)
10.3390/ijerph6020492
Emerging Patient-Driven Health Care Models: An Examination of Health Social Networks, Consumer Personalized Medicine and Quantified Self-Tracking
M. Swan (2009)
10.1111/j.1365-2869.2011.00944.x
Validation of an automated wireless system to monitor sleep in healthy adults
John R Shambroom (2012)
10.1371/journal.pone.0023473
Efficient Replication of over 180 Genetic Associations with Self-Reported Medical Data
J. Tung (2011)
10.2196/jmir.1687
Use of an Online Community to Develop Patient-Reported Outcome Instruments: The Multiple Sclerosis Treatment Adherence Questionnaire (MS-TAQ)
P. Wicks (2011)
10.2196/jmir.1053
Social Uses of Personal Health Information Within PatientsLikeMe, an Online Patient Community: What Can Happen When Patients Have Access to One Another’s Data
Jeana H Frost (2008)
10.1017/S0140525X04000068
Self-experimentation as a source of new ideas: ten examples about sleep, mood, health, and weight.
S. Roberts (2004)
10.3758/s13428-011-0081-0
The viability of crowdsourcing for survey research
T. Behrend (2011)
10.3109/17482968.2010.484494
Modifiable barriers to enrollment in American ALS research studies
R. Bedlack (2010)
10.1371/journal.pgen.1000965
Consent and Internet-Enabled Human Genomics
G. Gibson (2010)
10.1038/nbt1009-888
The power of social networking in medicine
Catherine A. Brownstein (2009)
10.1038/nbt.1837
Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm
P. Wicks (2011)
10.22323/2.09010206
Googling your genes: personal genomics and the discourse of citizen bioscience in the network age
M. Levina (2010)
10.1007/s11999-010-1373-x
The Potential Research Impact of Patient Reported Outcomes on Osteogenesis Imperfecta
Catherine A. Brownstein (2010)
10.1002/mds.22528
Pathological gambling amongst Parkinson's disease and ALS patients in an online community (PatientsLikeMe.com)
P. Wicks (2009)
10.1016/j.mehy.2010.04.030
The unreasonable effectiveness of my self-experimentation.
S. Roberts (2010)
10.1136/jnnp.2010.208413
Concordance between site of onset and limb dominance in amyotrophic lateral sclerosis
M. Turner (2010)
10.1371/journal.pgen.1002141
Web-Based Genome-Wide Association Study Identifies Two Novel Loci and a Substantial Genetic Component for Parkinson's Disease
Chuong B. Do (2011)
10.1177/1545968311425908
The Promise of mHealth
B. Dobkin (2011)
10.1109/MIS.2009.36
The Unreasonable Effectiveness of Data
A. Halevy (2009)
The Wisdom of Patients: Health Care Meets Online Social Media
J. Sarasohn-Kahn (2008)
10.2196/jmir.1643
Patient-reported Outcomes as a Source of Evidence in Off-Label Prescribing: Analysis of Data From PatientsLikeMe
J. Frost (2011)
10.2196/jmir.1549
Sharing Health Data for Better Outcomes on PatientsLikeMe
P. Wicks (2010)
10.1111/j.1468-1331.2008.02434.x
Measuring function in advanced ALS: validation of ALSFRS‐EX extension items
P. Wicks (2009)
10.1097/01.NT.0000405140.28789.76
Can a Web-Based Recruitment Tool for Genomic Analysis be Valid?
Jamie Talan (2011)
10.1073/pnas.0708022105
Lithium delays progression of amyotrophic lateral sclerosis
F. Fornai (2008)
10.1371/journal.pgen.1000993
Web-Based, Participant-Driven Studies Yield Novel Genetic Associations for Common Traits
N. Eriksson (2010)
10.1371/journal.pone.0024974
Smart Phone, Smart Science: How the Use of Smartphones Can Revolutionize Research in Cognitive Science
S. Dufau (2011)
10.2196/jmir.1056
Health 2.0 and Medicine 2.0: Tensions and Controversies in the Field
Benjamin Hughes (2008)



This paper is referenced by
Topic Modeling and Network Visualization to Explore Patient Experiences
A. Chen (2013)
10.1145/3313831.3376871
Future Opportunities for IoT to Support People with Parkinson's
R. McNaney (2020)
Participant selection in CrowdSensing environments Selección de participantes en ambientes CrowdSensing
Johan Andrés Mendoza Torres (2015)
ADVOCACY OF PHARMACIST TOWARDS CONCOMITANT SOCIAL MEDIA: MEETING AN URGE TO PATIENTS NEED & OUTCOME
Ravi Pratap Pulla (2017)
10.1111/jlme.12056
The Apomediated World: Regulating Research When Social Media Has Changed Research
Dan O'Connor (2013)
Applications of crowdsourcing in health
Kerri Wazny (2018)
10.15265/IY-2014-0004
Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives. Contribution of the IMIA Social Media Working Group.
M. Hansen (2014)
10.1111/1467-9566.12109
The commodification of patient opinion: the digital patient experience economy in the age of big data.
D. Lupton (2014)
10.2196/jmir.2513
Crowdsourcing Participatory Evaluation of Medical Pictograms Using Amazon Mechanical Turk
Bei Yu (2013)
Challenges and Opportunities in Designing Technology to Support ``Do-it-yourself'' Experimentation
Christina Kelley (2016)
PSICOLOGÍA 2.0: OPORTUNIDADES Y RETOS PARA EL PROFESIONAL DE LA PSICOLOGÍA EN EL ÁMBITO DE LA ESALUD
M. Armayones (2015)
10.3390/ijerph17062075
Budgeting for Environmental Health Services in Healthcare Facilities: A Ten-Step Model for Planning and Costing
D. Anderson (2020)
Is there a Doctor in the Crowd? Diagnosis Needed! (for less than $5)
James Cheng (2015)
Ontology-guided Health Information Extraction, Organization, and Exploration
Licong Cui (2014)
10.1007/978-1-4614-8806-4_12
The Virtuous Circle of the Quantified Self: A Human Computational Approach to Improved Health Outcomes
P. Wicks (2013)
10.5392/JKCA.2013.13.11.807
Design Korean Medicine Health Information Model with Health 2.0 Framework
Sang-Jun Yea (2013)
WiSDM: a platform for crowd-sourced data acquisition, analytics, and synthetic data generation
Ananya Choudhury (2016)
10.1093/jamiaopen/ooz052
Research data management in health and biomedical citizen science: practices and prospects
A. Borda (2020)
10.2196/resprot.7565
Study of Methods for Assessing Research Topic Elicitation and pRioritization (SMARTER): Study Protocol to Compare Qualitative Research Methods and Advance Patient Engagement in Research
D. Lavallee (2017)
10.1007/s11892-013-0468-7
Innovative Uses of Electronic Health Records and Social Media for Public Health Surveillance
E. Eggleston (2014)
10.3390/jpm2030093
Health 2050: The Realization of Personalized Medicine through Crowdsourcing, the Quantified Self, and the Participatory Biocitizen
M. Swan (2012)
10.1136/bmj.f3007
Social networks, social media, and social diseases
E. Coiera (2013)
10.1016/J.SOCSCIMED.2019.112366
Collective self-experimentation in patient-led research: How online health communities foster innovation.
Joanna Kempner (2019)
Social Networking and Dental Care: State of the Art and Analysis of the Impact on Dentists, Dental Practices and their Patients
Sojen Pradhan (2013)
10.7189/jogh.08.010502
Applications of crowdsourcing in health: an overview
Kerri Wazny (2018)
10.1016/j.chest.2016.04.016
Sleep Tracking, Wearable Technology, and Opportunities for Research and Clinical Care.
A. Shelgikar (2016)
10.1145/3027063.3027071
Digital Health & Self-experimentation: Design Challenges & Provocations
Markéta Dolejsová (2017)
10.1051/MATECCONF/201712504018
Crowdsourcing as an IT help tool to determine impact in the health sector
Juan Diego López (2017)
10.3390/PR7080493
Key Points for an Ethical Evaluation of Healthcare Big Data
P. León-Sanz (2019)
10.1016/j.jbi.2014.03.006
Pharmaceutical drugs chatter on Online Social Networks
Matthew T. Wiley (2014)
10.1016/J.LISR.2015.11.005
Rituals of introduction and revolving roles: Socialization in an online breast cancer community
E. Rubenstein (2015)
10.17011/HT/URN.201711104211
Aligning information technologies with evidence-based health-care activities: A design and evaluation framework
K. Sedig (2017)
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