Randy M Burd*, University of Arizona, Tucson, United States Donella Yoon, University of Arizona, Tucson, United States Melanie D Hingle, University of Arizona, Tucson, United States Track: Research Presentation Topic: Web 2.0 approaches for behaviour change, public health and biosurveillance Presentation Type: Rapid-Fire Presentation Submission Type: Single Presentation Building: Joseph B. Martin Conference Center at Harvard Medical School Room: C-Rotunda Room Date: 2012-09-15 04:00 PM – 04:45 PM Last modified: 2012-09-15

If you are the presenter of this abstract (or if you cite this abstract in a talk or on a poster), please show the QR code in your slide or poster (QR code contains this URL).

Background
Innovative and tailored approaches to health promotion are needed to help individuals identify and overcome barriers to healthy eating. Increasing an individual’s awareness of their typical dietary habits and reasons for making food-related decisions represent first steps toward this goal. Mobile technologies allow diet-conscious individuals to track their intake in real time from any location. Despite the large number of software applications available to consumers that promote healthy eating, the efficacy of these applications have not yet been empirically evaluated.

Objective
The purpose of our project was to test the feasibility and acceptability of a popular social media network – Twitter- to capture all foods and beverages consumed and reasons for choosing these foods (including related contextual data) in a convenience sample of adults. A secondary aim was to capture and analyze participant data from Twitter using a novel analytic tool designed to identify patterns in intake and behavior, and relationships between foods and contextual factors.

Methods
Participants were trained to record all food and beverages consumed over 3 consecutive days using their mobile device’s native Twitter application. A preset list of 25 hash tags which represented food groups and reasons for eating (e.g., #protein and #mood, respectively) were provided to participants to ensure standardized reporting. Participants were encouraged to annotate hashtags with descriptions (<140 characters), photos, or links to provide contextual information. Using our analytical software tool, participant data were captured from the public Twitter stream to determine the frequency of hash tag occurrence, co-occurrence, and to examine any related contextual data. Participants completed a brief survey at the end of the study that assessed the user experience.

Results
More than 55% of participants exclusively used their mobile device to record food consumption and related behaviors. More than 60% of users rated Twitter as easy to use for this purpose. The hashtags #grains, #sweets, and #protein were the most frequently used food tags, while #convenience, #taste, and #mood were most popular reasons for eating. An association matrix was used to determine the most commonly co-reported food and behavior tags, which suggested possible associations. Most participants used a combination of study-provided hash-tags and their own to describe behavior. Survey data indicated participants would like to see their own tweets presented in a graph or similar data visualization format, with the ability to track over a longer duration to look at trends.

Conclusions
Twitter provides a simple, flexible, efficient, and user-friendly method for capturing real-time dietary behavior. These findings will inform the design and sample size of a larger study exploring the relationship between food consumption, reasons for engaging in specific food-related behaviors, relevant contextual factors and weight status.