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 Nutrition for Precision Health (NPH) powered by All of Us research study is to develop Artificial Intelligence/Machine Learning (AI/ML) algorithms that predict individual responses to diet patterns using rich multimodal data streams collected across multiple domains (e.g., behavior, social, environmental, clinical and molecular biomarkers). NPH includes a large phenotyping cohort (Module 1, N=8000) and two separate follow-up groups drawn from a subset of Module 1participants. One group (Module 2, N=1200) receives three distinct diets in a 14-day crossover sequence, with at least a 14-day washout period between diets, while living in their own homes. A second group (Module 3, N=150) receives the same three diets under full-time supervision in a residential research setting. We will train and test AI/ML models to predict 0-4 hour postprandial response curves for glucose, insulin, triglycerides, and GLP-1, to the standardized diet-specific meal test (DSMT) collected after each of the three different diets delivered in Module 2. Each diet functions as a controlled stimulus to reveal biological features (such as individual variables, patterns, or clusters of measurements) that best predict a person's response. The Module 2 DSMT response curves are the primary outcomes (dependent variables) for AI/ML algorithms that predict individual responses to diet patterns. As a secondary objective, NPH will evaluate the validity and acceptability of technology-based dietary assessment tools. The Automated Self-Administered 24-hour recall (ASA24), Automatic Ingestion Monitor-2 (AIM-2), and the mobile food record (mFR) will be evaluated in Modules 2 and 3, and the ASA24 food record and the image-assisted ASA24 recall will be evaluated only in Module 3. Total energy intake, macronutrient and dietary fiber intake data are the main outcomes for validity testing compared against measures of actual intake. Acceptability will be determined from feedback surveys.
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.