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Background: Recruiting older adults (OA) into research is challenging.
Objective: To assess the feasibility of using two crowdsourcing platforms, Amazon's Mechanical Turk (MTurk) and Prolific Academic (ProA), as efficient and low-cost venues for recruiting survey participants aged 65 and older.
Methods: We developed an online survey to investigate and compare the demographics, technology use, and motivations for research participation of OA on MTurk and ProA. Qualitative responses, response time, word count, and recruitment costs were analyzed.
Results: We recruited 97 OA survey participants on both MTurk and ProA. Participants were similar in terms ofdemographics, technology usage, and motivations for participation (topic interest and payment).
Conclusion: Both crowdsourcing platforms are useful for rapid and low-cost recruitment of OA. The OA recruitment process was more efficient with ProA. Crowdsourcing platforms are potential sources of OA research participants; however, the pool is limited to generally healthy, technologically active, and well-educated older adults.
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Insects
August 2025
Biological Sciences, Bishop's University, 2600 College Street, Sherbrooke, QC J1M 1Z7, Canada.
Ongoing changes in the distribution and abundance of several tick species of medical relevance in Canada have prompted the development of the eTick platform-an image-based crowd-sourcing public surveillance tool for Canada enabling rapid tick species identification by trained personnel, and public health guidance based on tick species and province of residence of the submitter. Considering that more than 100,000 images from over 73,500 identified records representing 25 tick species have been submitted to eTick since the public launch in 2018, a partial automation of the image processing workflow could save substantial human resources, especially as submission numbers have been steadily increasing since 2021. In this study, we evaluate an end-to-end artificial intelligence (AI) pipeline to support tick identification from eTick user-submitted images, characterized by heterogeneous quality and uncontrolled acquisition conditions.
View Article and Find Full Text PDFCan J Cardiol
July 2025
Department of Emergency Medicine, National Taiwan University Hospital, Taipei City, Taiwan; Department of Emergency Medicine, National Taiwan University Hospital, Yun-Lin Branch, Douliu City, Taiwan.
Background: Timely defibrillator delivery for out-of-hospital cardiac arrests (OHCAs) remains challenging, with most cases relying on emergency medical services (EMSs) for response. This simulation study examined the feasibility of utilizing food delivery (FD) scooter riders as first responders for defibrillator delivery in OHCA incidents and compared simulated defibrillator arrival times to documented times.
Methods: This simulation study was conducted in Taipei, a densely populated city with a high concentration of scooter-based FD riders.
J Med Internet Res
August 2025
Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, 12 Science Drive 2, #10-01, Singapore, 117549, Singapore, 65 91878576.
Online surveys have become a key tool of modern health research, offering a fast, cost-effective, and convenient means of data collection. It enables researchers to access diverse populations, such as those underrepresented in traditional studies, and facilitates the collection of stigmatized or sensitive behaviors through greater anonymity. However, the ease of participation also introduces significant challenges, particularly around data integrity and rigor.
View Article and Find Full Text PDFAesthet Surg J
August 2025
Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada.
Background: One of the critical steps in abdominoplasty or reconstructive procedures using abdominal tissue involves repositioning the belly button. However, there is limited knowledge about patient satisfaction regarding the appearance of the belly button after such procedures. Currently, there is no standardized patient-reported outcome measure designed to assess this outcome.
View Article and Find Full Text PDFJ Clin Sleep Med
August 2025
Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA.
Study Objectives: To compare the performance of a comprehensive automated polysomnogram (PSG) analysis algorithm-CAISR (Complete Artificial Intelligence Sleep Report)-to a multi-expert gold standard panel, crowdsourced scorers, and experienced technicians for sleep staging and detecting arousals, respiratory events, and limb movements.
Methods: A benchmark dataset of 57 PSG records (Inter-Scorer Reliability dataset) with 200 30-second epochs scored per AASM guidelines was used. Annotations were obtained from (1) the AASM multi-expert gold standard panel, (2) AASM Inter-Scorer Reliability (ISR) platform users ("crowd," averaging 6,818 raters per epoch), (3) three experienced technicians, and (4) CAISR.