MyBehavior is a mobile application with a suggestion engine that learns a user's physical
activity and dietary behavior, and provides finely-tuned personalized suggestions. To our
knowledge, MyBehavior is the first smartphone app to provide personalized health suggestions
automatically, going beyond commonly used one-size-fits-all prescriptive approaches, or
tailored interventions from health-care professionals. MyBehavior uses an online multi-armed
bandit model to automatically generate context-sensitive and personalized activity/food
suggestions by learning the user's actual behavior. The app continually adapts its
suggestions by exploiting the most frequent healthy behaviors, while sometimes exploring
non-frequent behaviors, in order to maximize the user's chance of reaching a health goal
(e.g. weight loss).
A dramatic rise in self-tracking applications for smartphones has occurred recently. Rich
user interfaces make manual logging of users' behavior easier and more pleasant; sensors
make tracking effortless. To date, however, feedback technologies have been limited to
providing counts or attractive visualization of tracked data. Human experts (health
coaches) have needed to interpret the data and tailor make customized recommendations. No
automated recommendation systems like Pandora, Netflix or personalized search for the web
have been available to translate self-tracked data into actionable suggestions that promote
healthier lifestyle without needing to involve a human interventionist.
MyBehavior aims to fill this gap. It takes a deeper look into physical activity and dietary
intake data and reveal patterns of both healthy and unhealthy behavior that could be
leveraged for personalized feedback. Based on common patterns from a user's life,
suggestions are created that ask users to continue, change or avoid existing behaviors to
achieve certain fitness goals. Such an approach is different from existing literature in two
important aspects: (1) suggestions are contextualized to a user's life and are built on
existing user behaviors. As a result, users can act on these suggestions easily, with
minimal effort and interruption to daily routines; (2) unique suggestions are created for
each individual. This personalized approach differs from traditional one-size-fits-all or
targeted intervention models where identical suggestions are applied for groups of similar
people or the entire population.
- In relatively healthy condition. Also, users must be interested in health and
- Individuals with physical disability and dietary problems are excluded.