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; UMass Worcester Prevention Research Center
Publication Date
3-7-2016
Document Type
Article
Disciplines
Behavior and Behavior Mechanisms | Health Communication | Health Information Technology | Public Health Education and Promotion | Translational Medical Research
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.
Keywords
UMCCTS funding, computer-tailored health communication, machine learning, recommender systems
Rights and Permissions
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.
DOI of Published Version
10.2196/jmir.4448
Source
J Med Internet Res. 2016 Mar 7;18(3):e42. doi: 10.2196/jmir.4448. Link to article on publisher's site
Journal/Book/Conference Title
Journal of medical Internet research
Related Resources
PubMed ID
26952574
Repository Citation
Sadasivam, Rajani S.; Cutrona, Sarah L.; Kinney, Rebecca L.; Marlin, Benjamin M.; Mazor, Kathleen M.; Lemon, Stephenie C.; and Houston, Thomas K., "Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century" (2016). UMass Center for Clinical and Translational Science Supported Publications. 81.
https://escholarship.umassmed.edu/umccts_pubs/81
Included in
Behavior and Behavior Mechanisms Commons, Health Communication Commons, Health Information Technology Commons, Public Health Education and Promotion Commons, Translational Medical Research Commons