Table 1.

Rule-based computer-tailored health communication (CTHC) versus recommender systems.

Feature	Rule-based CTHC	Recommender systems
Intervention development questions	(1) Message writing: What are the important concepts for the targeted population?	(2) Message writing: What are the important concepts for the targeted population?
	(2) Tailoring variables: How should the target population be segmented?	(2) Tailoring variables: What collective-intelligence data (implicit and explicit data) should be collected and how?
	(3) Rules: How should messages for the participant patient segment be selected?
Message selection	Rules-driven: Study designers develop rules based on the literature and theory. These rules link user profiles to the metadata of the messages, selecting messages for a patient subset.	Data-driven: Sophisticated machine learning algorithms derive the tailoring rules from the collective-intelligence data of the individual, as well as the group.
Complexity (number of variables)	The number of variables incorporated can become quickly unmanageable. It is limited by the sophistication of the study designers in the team, project’s timeline, and budget.	Sophisticated algorithms can potentially consider all the variables collected in the intervention.
Use of theory	Tailoring is limited to theoretical constructs.	Theory is augmented by deriving recommendations from the user data.
Adaptation	System is limited to predicted changes in behavior.	System can continuously adapt, potentially improving with each message delivered. Responds to the user’s behavior and to the group’s behavior over time.