Modeling Interdependent and Periodic Real-World Action Sequences

Mobile health applications, including those that track activities such as exercise, sleep, and diet, are becoming widely used. Accurately predicting human actions is essential for targeted recommendations that could improve our health and for personalization of these applications. However, making such predictions is extremely difficult due to the complexities of human behavior, which consists of a large number of potential actions that vary over time, depend on each other, and are periodic. Previous work has not jointly modeled these dynamics and has largely focused on item consumption patterns instead of broader types of behaviors such as eating, commuting or exercising. In this work, we develop a novel statistical model for Time-varying, Interdependent, and Periodic Action Sequences. Our approach is based on personalized, multivariate temporal point processes that model time-varying action propensities through a mixture of Gaussian intensities. Our model captures short-term and long-term periodic interdependencies between actions through Hawkes process-based self-excitations. We evaluate our approach on two activity logging datasets comprising 12 million actions taken by 20 thousand users over 17 months. We demonstrate that our approach allows us to make successful predictions of future user actions and their timing. Specifically, our model improves predictions of actions, and their timing, over existing methods across multiple datasets by up to 156%, and up to 37%, respectively. Performance improvements are particularly large for relatively rare and periodic actions such as walking and biking, improving over baselines by up to 256%. This demonstrates that explicit modeling of dependencies and periodicities in real-world behavior enables successful predictions of future actions, with implications for modeling human behavior, app personalization, and targeting of health interventions.Comment: Accepted at WWW 201

Goal-setting And Achievement In Activity Tracking Apps: A Case Study Of MyFitnessPal

Activity tracking apps often make use of goals as one of their core motivational tools. There are two critical components to this tool: setting a goal, and subsequently achieving that goal. Despite its crucial role in how a number of prominent self-tracking apps function, there has been relatively little investigation of the goal-setting and achievement aspects of self-tracking apps. Here we explore this issue, investigating a particular goal setting and achievement process that is extensive, recorded, and crucial for both the app and its users' success: weight loss goals in MyFitnessPal. We present a large-scale study of 1.4 million users and weight loss goals, allowing for an unprecedented detailed view of how people set and achieve their goals. We find that, even for difficult long-term goals, behavior within the first 7 days predicts those who ultimately achieve their goals, that is, those who lose at least as much weight as they set out to, and those who do not. For instance, high amounts of early weight loss, which some researchers have classified as unsustainable, leads to higher goal achievement rates. We also show that early food intake, self-monitoring motivation, and attitude towards the goal are important factors. We then show that we can use our findings to predict goal achievement with an accuracy of 79% ROC AUC just 7 days after a goal is set. Finally, we discuss how our findings could inform steps to improve goal achievement in self-tracking apps

Service-oriented coordination platform for technology-enhanced learning

It is currently difficult to coordinate learning processes, not only because multiple stakeholders are involved (such as students, teachers, administrative staff, technical staff), but also because these processes are driven by sophisticated rules (such as rules on how to provide learning material, rules on how to assess students’ progress, rules on how to share educational responsibilities). This is one of the reasons for the slow progress in technology-enhanced learning. Consequently, there is a clear demand for technological facilitation of the coordination of learning processes. In this work, we suggest some solution directions that are based on SOA (Service-Oriented Architecture). In particular, we propose a coordination service pattern consistent with SOA and based on requirements that follow from an analysis of both learning processes and potentially useful support technologies. We present the service pattern considering both functional and non-functional issues, and we address policy enforcement as well. Finally, we complement our proposed architecture-level solution directions with an example. The example illustrates our ideas and is also used to identify: (i) a short list of educational IT services; (ii) related non-functional concerns; they will be considered in future work