The excessive intake of saturated fat and added sugars has been identified as a leading cause of premature
mortality among adults in the U.S. contributing to approximately 700,000 deaths each year. The 2015-2020
Dietary Guidelines for Americans recommend limiting these nutrients to <10% total energy intake to prevent
disease. Achieving these public health recommendations will require understanding the patterns of saturated fat
and added sugar intake so more effective dietary interventions can be developed. Traditionally, estimates of
saturated fat and added sugar intake are measured using food frequency questionnaires or 24-hr dietary recalls
(24HR). These methods are time-intensive and cognitively taxing for users and costly for researchers. They are
also highly prone to recall bias and misreporting related due, in part, to the reliance on a person’s memory over
long recall intervals and errors in portion size estimation. The proposed dietary assessment method aims to
address these limitations with ecological momentary assessment (EMA). EMA uses updated technology and
sampling methods that can update and improve upon traditional assessment methods. EMA studies often use
mobile phone apps to assess events with brief, automated surveys delivered periodically throughout the day.
EMA can, thereby, shorten recall intervals to improve reporting errors and reduce user and researcher burden
while maximizing the ecological validity. To date, mobile EMA methods for diet assessment (mEMDA) used in
research have been study specific. They have not been systematically developed nor optimized for widespread
use in research. This project would represent the first research-quality and fully automated, EMA-based mobile
dietary assessment research tool. In recent pilot work, we demonstrated the potential utility of mEMDA. A brief
mobile survey performed as well as web-assisted 24HR to estimate the intake of predefined snack foods. Here,
the goal of the proposed project is to systematically develop and test a mEMDA app and sampling approach to
accurately estimate the intake of saturated fat and added sugars in a diverse population. To do this we will derive
a culturally- and demographically representative list of foods and beverages that contribute a majority (>70%) of
the saturated fat and added sugars in the American diet using recent NHANES data (Aim 1); develop with a
user-centered design the mEMDA app and analysis platform with visual food images for portion size estimation
and nutrient analysis capabilities (Aim 2); determine the best mEMDA sampling approach (event-contingent vs.
interval-contingent sampling) (Aim 3); and compare the accuracy of estimating energy intake from saturated fat
and added sugars using the optimized mEMDA app and sampling approach vs. interviewer-assisted 24HR in a
controlled-feeding study. Future applications of the mEMDA app include: (1) reliably assessing momentary
intakes of other foods or nutrients (e.g., fruit and vegetable intake, sodium), (2) integration with mobile
intervention platforms to give real-time, dietary feedback to participants (3) concurrent-capturing meal context
variables (e.g., social, environmental, and psycho-social variables) for future, just-in-time dietary interventions.
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