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Review
. 2016 Mar 7;18(3):e42.
doi: 10.2196/jmir.4448.

Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century

Rajani Shankar Sadasivam  1 , Sarah L Cutrona, Rebecca L Kinney, Benjamin M Marlin, Kathleen M Mazor, Stephenie C Lemon, Thomas K Houston
Affiliations
Review

Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century

Rajani Shankar Sadasivam et al. J Med Internet Res. .

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: computer-tailored health communication; machine learning; recommender systems.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Literature review study flow diagram.
Figure 2
Figure 2
Structure of a current rule-based computer-tailored health communication (CTHC) system.
Figure 3
Figure 3
Components of recommender systems for computer tailoring. The primary differences between the 2 systems depicted in Figure 2 and 3 are shaded in gray. They represent the continuous feedback data that the recommender systems are able to use. CTHC: computer-tailored health communication.
See this image and copyright information in PMC

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