Clinical trial NCT02359981: MyBehavior: Persuasion by Adapting to User Behavior and User Preference
Official title: MyBehavior: Persuasion by Adapting to User Behavior and User Preference
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Weight Loss
Keywords: Weight loss, Fitness
Lead sponsor: Cornell University
Sponsor class: OTHER
First posted: 2015-02-10
Enrollment: 17
Sex: ALL
Minimum age (years): 18
Maximum age (years): 60
Standard ages: ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: PREVENTION

Brief 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.

Eligibility criteria: Inclusion Criteria:

* In relatively healthy condition. Also, users must be interested in health and fitness.

Exclusion Criteria:

* Individuals with physical disability and dietary problems are excluded.

Primary outcomes:
measure: User intentions to follow automated suggestions and behavior change
timeFrame: 3 weeks
Secondary outcomes:
measure: Usability improvements of automated suggestions
timeFrame: 3 weeks
Other outcomes:
Locations:
city: Ithaca
country: United States
facility: Cornell University
state: New York
Study officials:
affiliation: Cornell University
name: Mashfiqui Rabbi, BS
role: PRINCIPAL_INVESTIGATOR
References:
citation: Rabbi M, Pfammatter A, Zhang M, Spring B, Choudhury T. Automated personalized feedback for physical activity and dietary behavior change with mobile phones: a randomized controlled trial on adults. JMIR Mhealth Uhealth. 2015 May 14;3(2):e42. doi: 10.2196/mhealth.4160.