Dietary Habits clinical trials at University of California Health
2 in progress, 1 open to eligible people
Nutrition for Precision Health, Powered by the All of Us
open to eligible people ages 18 years and up
The goal of this investigational study is to develop algorithms that predict human response to foods. The main question it aims to answer are: - How does varying foods and eating patterns impact one's biological and physiological responses? - In what ways can novel dietary assessment measures be used to improve dietary assessments and to prescribe assessments to people in future research with increased precision? - Can artificial intelligence and machine learning techniques be combined to prescribe foods and eating patterns to individuals for optimization of their health? There are 3 Modules participants may take part in: - Module 1- A participant's dietary intake and accompanying nutritional status, biological and other measures will be observed over 10 days, as well as physiological responses to a liquid mixed meal tolerance test will be measured. - Module 2- Participants will undergo three controlled dietary interventions provided for 14-days each and separated by washout periods of at least 14 days. Physiological responses following a diet-specific meal test will be measured. - Module 3- Participants will undergo the same three dietary interventions during the same 14 day periods as Module 2 while being studied in-residence. Physiological responses following a liquid mixed meal tolerance test and a diet-specific meal test will be measured.
at UC Davis UCLA UCSD
Front-of-package Nutrient Labels
Sorry, not yet accepting patients
This study aims to compare different front-of-package label designs, using two schemes: (1) High In and (2) Nutrition Info with each scheme having (1) a version with colors (i.e., green, yellow, and/or red) indicating level of nutrient content and (2) a black-and-white version. Additionally the Nutrition Info scheme will have a version that includes the percent Daily Value in black and white. Labels will be compared against a no-label control and one another.
at UC Davis
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