With an increased demand for personalized systems, adaptive systems can support its users by recognizing their cognitive state, and adapt different elements to improve the user's mental state. With a literature survey, the following question has been answered: how do intelligent systems acquire and use information related to user attention? The answer was found by following the PRISMA guidelines, using the identification flowchart, to ensure reproducibility of the identification and screening of the papers, in addition to applying consistent criteria to which articles could be included in the review. This process resulted in 74 papers that fit the criteria. The results showed a large variety in the sensor input, modeling, objectives and domain, while the adaptation strategies could be summarized by five categories: UI change, feedback timing, automation level, difficulty adjustment and behavioral feedback. Combinations of categories were also present. UI changes and feedback timing were the most popular categories, especially from 2015 onward. Difficulty adjustments were surprisingly rarely utilized, especially in articles focused on education, possibly because the adaptation requires additional complexity to be added to the system. Challenges described in the literature were mainly focused on short term improvements, instead of long term issues.