MyBehavior: Persuasion by Adapting to User Behavior and User Preference
Condition: Weight Loss
MeSH terms: Weight Loss
Study type: Interventional

Summary: 
      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).
    

Detailed description: 
      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.
    

Intervention name(s): MyBehavior; Generic suggestions; Smartphone
Intervention type(s): Behavioral; Behavioral; Device
Intervention description(s): The intervention automatically provides personalized suggestions based on users behavior and user context. Suggestions relates to users life and how often they have done them in the past. Since the suggestions relate to users' lives, they are easy to follow.; A nutritionist and an exercise trainer jointly created 45 food and exercise suggestions based on guidelines posted by the NIH. These suggestions ask users to walk for 30 minutes or eat healthier foods. These suggestions however doesn't personalize to users daily behavior into account.; An Android Smartphone with operating system version higher than 2.2
Allocation: Randomized
Intervention model: Parallel Assignment
Masking: Single (Participant)
Primary purpose: Prevention
Keywords: Weight loss, Fitness