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Recruitment and Retention in Remote Research: Learnings From a Large, Decentralized Real-world Study.

Sophia Xueying LiRamzi HalabiRahavi SelvarajanMolly WoernerIsabell Griffith FillipoSreya BanerjeeBrittany MosserFelipe Ananda JainPatricia A AreánAbhishek Pratap
Published in: JMIR formative research (2022)
Our findings highlight several factors, such as recruitment platforms, incentive distribution frequency, the timing of baseline surveys, device heterogeneity, and technical glitches in data collection infrastructure, that could impact remote long-term data collection. Combined together, these empirical findings could help inform best practices for monitoring anomalies during real-world data collection and for recruiting and retaining target populations in a representative and equitable manner.
Keyphrases
  • electronic health record
  • big data
  • healthcare
  • primary care
  • cross sectional
  • single cell
  • data analysis
  • machine learning