MyBehavior: Persuasion by Adapting to User Behavior and User Preference

NCT ID: NCT02359981

Last Updated: 2015-02-11

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 17 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2013-05-31
• Study Completion Date: 2013-06-30

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.

Conditions

Weight Loss

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: PREVENTION
• Blinding Strategy: SINGLE

Study Groups

Generic suggestions

Control group participants received suggestions generated by the a nutritionist and exercise trainer. These suggestions didn't relate to user's life or their past behavior.

Group Type: ACTIVE_COMPARATOR

Generic suggestions

Intervention Type: BEHAVIORAL

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.

Smartphone

Intervention Type: DEVICE

An Android Smartphone with operating system version higher than 2.2

MyBehavior

Experiment group participants received personalized suggestions from MyBehavior that relates their life and past behavior.

Group Type: EXPERIMENTAL

MyBehavior

Intervention Type: BEHAVIORAL

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.

Smartphone

Intervention Type: DEVICE

An Android Smartphone with operating system version higher than 2.2

Interventions

MyBehavior

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.

Intervention Type: BEHAVIORAL

Generic suggestions

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.

Intervention Type: BEHAVIORAL

Smartphone

An Android Smartphone with operating system version higher than 2.2

Intervention Type: DEVICE

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.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 60 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Cornell University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Mashfiqui Rabbi, BS

Role: PRINCIPAL_INVESTIGATOR

Cornell University

Locations

Cornell University

Ithaca, New York, United States

Countries

United States

References

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.

Reference Type: DERIVED

PMID: 25977197 (View on PubMed)

Other Identifiers

1302003617

Identifier Type: -

Identifier Source: org_study_id