MyBehavior: Persuasion by Adapting to User Behavior and User Preference

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).

Study Overview

• Status: Completed
• Conditions: Weight Loss
• Intervention / Treatment: Behavioral: Generic suggestions; Device: Smartphone; Behavioral: MyBehavior

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.

• Study Type: Interventional
• Enrollment (Actual): 17
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • New York
    • Ithaca, New York, United States, 14850
      • Cornell University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 60 years (Adult)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

Description

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.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Prevention
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Active Comparator: 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.	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.; Device: Smartphone; An Android Smartphone with operating system version higher than 2.2
Experimental: MyBehavior; Experiment group participants received personalized suggestions from MyBehavior that relates their life and past behavior.	Device: Smartphone; An Android Smartphone with operating system version higher than 2.2; Behavioral: 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
User intentions to follow automated suggestions and behavior change	The primary outcome is to measure efficacy of MyBehavior suggestions. Efficacy will be measured in two dimensions (1) whether users intend to follow the automated suggestions from MyBehavior (2) effectiveness of automated suggestions in actual behavior change. User intentions towards following MyBehavior suggestions are measured using a 5 point likert scale. The investigators will ask users to rate whether they can follow the suggestions on an average day within a scale of 1-5 (1- I can't follow the suggestion, 5 - I can easily follow the suggestion). On the other hand, behavior change is measured from food (calories in per meal consumed) and activity (walking, running or exercise durations per day etc.) log collected using their smartphone. Regarding physical activity, how much physical activity users are performing will be compared across experiment conditions. Similarly, calorie consumption change in food will be used to compare dietary behavior change.	3 weeks

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Usability improvements of automated suggestions	MyBehavior is the first system to provide health suggestions for food and activity automatically. Thus there are scopes of usability improvement on how to effectively present the automatically generated information to the user. Qualitative interviews at the end of study will be conducted to gather user experience of using MyBehavior. This interviews will help to build a better and more usable version of MyBehavior for future larger scale deployments.	3 weeks

Collaborators and Investigators

• Sponsor: Cornell University
• Investigators: Principal Investigator: Mashfiqui Rabbi, BS, Cornell University

Publications and helpful links

General Publications

• 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.

Study record dates

Study Major Dates

• Study Start: May 1, 2013
• Primary Completion (Actual): June 1, 2013
• Study Completion (Actual): June 1, 2013

Study Registration Dates

• First Submitted: February 2, 2015
• First Submitted That Met QC Criteria: February 5, 2015
• First Posted (Estimate): February 10, 2015

Study Record Updates

• Last Update Posted (Estimate): February 11, 2015
• Last Update Submitted That Met QC Criteria: February 10, 2015
• Last Verified: February 1, 2015

More Information

Terms related to this study

• Keywords: Weight loss, Fitness
• Additional Relevant MeSH Terms: Body Weight; Body Weight Changes; Weight Loss
• Other Study ID Numbers: 1302003617