The Promise and Pitfalls of Using Crowdsourcing in Research Prioritization for Back Pain: Cross-Sectional Surveys
Abstract
Background: The involvement of patients in research better aligns evidence generation to the gaps that patients themselves face when making decisions about health care. However, obtaining patients’ perspectives is challenging. Amazon’s Mechanical Turk (MTurk) has gained popularity over the past decade as a crowdsourcing platform to reach large numbers of individuals to perform tasks for a small reward for the respondent, at small cost to the investigator. The appropriateness of such crowdsourcing methods in medical research has yet to be clarified. Objective: The goals of this study were to (1) understand how those on MTurk who screen positive for back pain prioritize research topics compared with those who screen negative for back pain, and (2) determine the qualitative differences in open-ended comments between groups. Methods: We conducted cross-sectional surveys on MTurk to assess participants’ back pain and allow them to prioritize research topics. We paid respondents US $0.10 to complete the 24-point Roland Morris Disability Questionnaire (RMDQ) to categorize participants as those “with back pain” and those “without back pain,” then offered both those with (RMDQ score ≥7) and those without back pain (RMDQ
Keywords
low back pain
patient participation
medical
patient engagement
back pain
stakeholder engagement
comparative effectiveness research
crowdsourcing
Internet
research
medicine
journal
ehealth
JMIR
open access publishing
medical research
medical informaticsresearch prioritization
mturk
amazon mechanical turk
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112646
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112646
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doi:10.2196/jmir.8821
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