Impact of a Collective Intelligence Tailored Messaging System on Smoking Cessation: The Perspect Randomized Experiment.

Journal: Journal of medical Internet research
Published Date: Nov 8, 2016

Abstract

BACKGROUND: Outside health care, content tailoring is driven algorithmically using machine learning compared to the rule-based approach used in current implementations of computer-tailored health communication (CTHC) systems. A special class of machine learning systems ("recommender systems") are used to select messages by combining the collective intelligence of their users (ie, the observed and inferred preferences of users as they interact with the system) and their user profiles. However, this approach has not been adequately tested for CTHC.

Authors

  • Rajani Shankar Sadasivam
    Division of Health Informatics and Implementation Science, Quantitative Health Sciences, University of Massachusetts Medical Scool, Worcester, MA, United States.
  • Erin M Borglund
    Division of Health Informatics and Implementation Science, Quantitative Health Sciences, University of Massachusetts Medical Scool, Worcester, MA, United States.
  • Roy Adams
    College of Information and Computer Sciences, University of Massaachusttes Amherst, Amherst, MA, United States.
  • Benjamin M Marlin
    College of Information and Computer Sciences, University of Massaachusttes Amherst, Amherst, MA, United States.
  • Thomas K Houston
    Division of Health Informatics and Implementation Science, Quantitative Health Sciences, University of Massachusetts Medical Scool, Worcester, MA, United States.

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