Personalized technology for supporting health behaviors

Previous work within health communication has been concerned with how to tailor intervention content in a way that is most effective in supporting the individual in changing health behaviors such as smoking, physical activity or diet. This kind of tailoring is based on data gathered from the user through questionnaires with textual feedback adapted by algorithms pre-specified according to behavioral theory. The Internet opened up for a new generation of tailored interventions that were more sophisticated, more synchronous and more longitudinal. In the current paper I present the results of a scoping review to explore literature relevant to how the third generation of tailored interventions might look like, using sensors embedded in commonly available ICT such as PCs and smartphones and machine learning for tailoring these kinds of interventions in real time, thus becoming cognitive infocommunications (CogInfoCom) applications that enhance the human brain. The main aim of the paper is to inspire further multidisciplinary research and development on addressing the important topic of supporting the individual in changing and maintaining health behaviors.