Habitual behavior is often hard to change because of a lack of self-monitoring skills. Digital technologies offer an unprecedented chance to facilitate self-monitoring by delivering feedback on undesired habitual behavior. This review analyzed the results of 72 studies in which feedback from digital technology attempted to disrupt and change undesired habits. A vast majority of these studies found that feedback through digital technology is an effective way to disrupt habits, regardless of target behavior or feedback technology used.
Smartphones and similar devices allow access to a wealth of information. Navigating this wealth of information is problematic. Semantic locations, assigned to observed GPS user movements, can help in providing inforamtion that is useful to the user at a specific time or place. This paper shows how a stream of sensor data can be processed and interpreted to determine (i) the locations of interest for a user, such as home, work, etc, and (ii) to predict the expected future transitions between such locations. We have implemented our algorithms in a fully functional prototype smartphone app and backend, and we present results based on actual usage data gathered over the past few months. We conclude that inferred semantic location information allows a smart device to offer personalized, contextual, information without the need for the user to perform any explicit query. Our system is open source, and can be used to build context-aware recommender systems that suggest content which is at the right time and at the right place.
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