Objective: The purpose of this study was to compare the crowdsourcing platform Amazon Mechanical Turk (MTurk) with in-person recruitment and web-based surveys as a method to (1) recruit study participants and (2) obtain low-cost data quickly from chiropractic patients with chronic low back pain in the United States.

Methods: In this 2-arm quasi-experimental study, we used in-person clinical sampling and web-based surveys from a separate study (RAND sample, n = 1677, data collected October 2016 to January 2017) compared with MTurk (n = 310, data collected November 2016) as a sampling and data collection tool. We gathered patient-reported health outcomes and other characteristics of adults with chronic low back pain receiving chiropractic care. Parametric and nonparametric tests were run. We assessed statistical and practical differences based on P values and effect sizes, respectively.

Results: Compared with the RAND sample, the MTurk sample was statistically significantly younger (mean age 35.4 years, SD 9.7 vs 48.9, SD 14.8), made less money (24% vs 17% reported less than $30,000 annual income), and reported worst mental health than the RAND sample. Other differences were that the MTurk sample had more men (37% vs 29%), fewer White patients (87% vs 92%), more Hispanic patients (9% vs 5%), fewer people with a college degree (59% vs 68%), and patients were more likely to be working full time (62% vs 58%). The MTurk sample was more likely to have chronic low back pain (78% vs 66%) that differed in pain frequency and duration. The MTurk sample had less disability and better global health scores. In terms of efficiency, the surveys cost $2.50 per participant in incentives for the MTurk sample. Survey development took 2 weeks and data collection took 1 month.

Conclusion: Our results suggest that there may be differences between crowdsourcing and a clinic-based sample. These differences range from small to medium on demographics and self-reported health. The low incentive costs and rapid data collection of MTurk makes it an economically viable method of collecting data from chiropractic patients with low back pain. Further research is needed to explore the utility of MTurk for recruiting clinical samples, such as comparisons to nationally representative samples.

Author keywords: Crowdsourcing, Back Pain, Chiropractic, Spine

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