This clinical trial focuses on testing the efficacy of different digital interventions to promote re-engagement in cancer-related long-term follow-up care for adolescent and young adult (AYA) survivors of childhood cancer.
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.
Nutrition for Precision Health, Powered by the All of Us Research Program
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
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Sponsor: RTI International
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.