The Promise and Pitfalls of Using Crowdsourcing in Research Prioritization for Back Pain: Cross-Sectional Surveys

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ID: 112646
2017
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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
Reference Key
lavallee2017journalthe Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Danielle C Lavallee;
Journal Journal of medical Internet research
Year 2017
DOI
doi:10.2196/jmir.8821
URL
http://doi.org/10.2196/jmir.8821
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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