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Article
Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century
UMass Center for Clinical and Translational Science Supported Publications
  • Rajani S. Sadasivam, University of Massachusetts Medical School
  • Sarah L. Cutrona, University of Massachusetts Medical School
  • Rebecca L. Kinney, University of Massachusetts Medical School
  • Benjamin M. Marlin, University of Massachusetts Amherst
  • Kathleen M. Mazor, University of Massachusetts Medical School
  • Stephenie C. Lemon, University of Massachusetts Medical School
  • Thomas K. Houston, University of Massachusetts Medical School
UMMS Affiliation
Division of Health Informatics and Implementation Science, Department of Quantitative Health Science; Meyers Primary Care Institute; Department of Medicine, Division of General Internal Medicine; Department of Medicine, Division of Preventive and Behavioral Medicine
Date
3-7-2016
Document Type
Article
Abstract

BACKGROUND: What is the next frontier for computer-tailored health communication (CTHC) research? In current CTHC systems, study designers who have expertise in behavioral theory and mapping theory into CTHC systems select the variables and develop the rules that specify how the content should be tailored, based on their knowledge of the targeted population, the literature, and health behavior theories. In collective-intelligence recommender systems (hereafter recommender systems) used by Web 2.0 companies (eg, Netflix and Amazon), machine learning algorithms combine user profiles and continuous feedback ratings of content (from themselves and other users) to empirically tailor content. Augmenting current theory-based CTHC with empirical recommender systems could be evaluated as the next frontier for CTHC.

OBJECTIVE: The objective of our study was to uncover barriers and challenges to using recommender systems in health promotion.

METHODS: We conducted a focused literature review, interviewed subject experts (n=8), and synthesized the results.

RESULTS: We describe (1) limitations of current CTHC systems, (2) advantages of incorporating recommender systems to move CTHC forward, and (3) challenges to incorporating recommender systems into CTHC. Based on the evidence presented, we propose a future research agenda for CTHC systems.

CONCLUSIONS: We promote discussion of ways to move CTHC into the 21st century by incorporation of recommender systems.

Rights and Permissions

Citation: J Med Internet Res. 2016 Mar 7;18(3):e42. doi: 10.2196/jmir.4448. Link to article on publisher's site

This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

Related Resources
Link to Article in PubMed
Keywords
  • UMCCTS funding,
  • computer-tailored health communication,
  • machine learning,
  • recommender systems
PubMed ID
26952574
Citation Information
Rajani S. Sadasivam, Sarah L. Cutrona, Rebecca L. Kinney, Benjamin M. Marlin, et al.. "Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century" Vol. 18 Iss. 3 (2016) ISSN: 1438-8871 (Linking)
Available at: http://works.bepress.com/stephenie_lemon/92/