Successful public health campaigns depend in large measure on how effectively information is communicated. Designing effective messages for the public's health is both an art and a science with the art dominating because knowledge generated by the science is accumulating too slowly and with insufficient theoretical guidance. The research proposed here abandons standard experimental approaches to message design and abandons theory development in favor of the development of a "recommendation machine" modeled after commercial systems. This approach will allow message recommendations tailored to individual preferences based on algorithms for content similarity, preference similarity or their combination. Recommendation systems are essentially derived algorithms operating on dense data involving both preferences for messages (ratings by smokers) and objective message features (content). Their goal is to predict a user's ratings for messages not previously seen by the user. Conventional approaches to message research advance the science of message design too slowly, are driven by inadequate theory, and require very complex factorial interactions among audience characteristics, message features and the target behavior. The development of a recommendation machine for health messages will operate on a large archive of messages, dense preference data from smokers, and extensive (and mostly automated) assessment of the objective features of messages. The results will provide a procedure for the selection of effective messages from a large archive that will be tailored to a specific target person. Unlike tailoring research, no a priori assumptions will be made about which audience characteristics would need to be identified to constrain message selection. Recommendation systems have the potential to transform research about effective messages. The outcomes would include (1) an algorithm for preferences for effective (smoking cessation) messages;(2) a leap beyond approaches to message design side-stepping the tedious work in one-feature-at-a-time experiments;(3) an approach employing methods familiar to anyone ever having bought a book on Amazon or selected a movie via Netflix;(4) setting the stage for automatic user friendly recommender systems. Message selection processes for behaviors to increase health and lower risk would change radically. Applications using new media such as mobile technologies and personalized health web sites would be enabled as well. The research proposed: (1) prepares existing data to use in pretesting recommendation systems;(2) develops recommendation algorithms that are hybrids of collaborative and content approaches using state-of- the-art procedures from the commercial arena;(3) tests hybrid algorithms in a sample of smokers comparing the preferences for recommended messages to two comparison conditions;(4) follows up to determine whether differences in smoking cessation intentions differ between those receiving messages suggested via the recommender algorithms vs. those receiving a random selection or a "most preferred" set.
Public health campaigns that seek to change behavior are successful in large part when the messages deployed are themselves effective by being attuned to the target audience and -- when possible -- the target individual. Conventional approaches to the design of effective messages in health behavior change -- primarily experimental and factorial in approach -- have moved too slowly both empirically and theoretically leaving message design unprincipled and intuitive. The research proposed radically alters the approach to message design and selection by developing empirically based recommendation algorithms based on a model of commercial product recommendation and applied and tested on a large archive of smoking cessation messages.
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