Weight loss is tricky and no one-size-fits-all solution exists. Patients become frustrated when the edict to “eat less and exercise more” doesn’t work. Personalized weight loss plans are needed but right now clinicians can’t predict which of the myriad of diets — intermittent fasting, ketogenic, Mediterranean, plant-based, and paleo — will work for individual patients.
A research team at UCLA is hoping to change that. The team is analyzing data from 550,000 users of the MyFitnessPal app across nearly 2 years to discern patterns in behaviors that lead to successful long-term weight loss. MyFitnessPal is a nutrition and fitness tracking app with billions of pieces of data about what and when people eat, drink, and exercise. The data will be used to develop a personalized approach to weight management to prevent cardiovascular disease, diabetes, and other obesity-related diseases.
“Our weight is truly a reflection of our entire life including our mood, activity level, living environment, stress, metabolism, genetics, sleep quality, and dietary quality,” said Zhaoping Li, MD, PhD, who is professor of medicine and chief of the Division of Clinical Nutrition at the University of California, Los Angeles. “With all those confounding factors, at any given time we may respond to one diet and not respond to another diet.”
The researchers will examine data points such as the number of times food is logged per week, if the user logs water intake, macronutrients intake, amount of exercise per week, types of exercise tracked, sleep, and mood. The data will then be segmented by age, gender, and other demographic information to better understand patterns in the populations of users likely to reach their goals.
The large sample size will allow for enough power to divide people into many different categories based on patterns found. The data from this large retrospective review will be used to determine personalized weight loss plans for optimal caloric and macronutrient intake as well as exercise for each category that will then be tested in a prospective randomized controlled trial.
The data shared with UCLA will be de-identified to preserve user privacy.
NIH Personalized Weight Loss Research
Dr Li and colleagues are also participating in the National Institutes of Health (NIH)-funded Nutrition for Precision Health study, which was launched in January 2022. The study is designed to test the effects of 3 diets on health outcomes: 1) traditional American diet; 2) Mediterranean plus DASH (Dietary Approaches to Stop Hypertension) diet; and 3) ketogenic diet. The goal is to determine which diet is best for overall health, weight management, preventing cardiovascular disease and diabetes, as well as other health outcomes. UCLA is 1 of the 14 clinical center enrollment sites nationwide.
The Nutrition for Precision Health study aims to examine individual differences observed in response to different diets by studying the interactions between diet, genes, proteins, microbiome, metabolism, and other individual contextual factors. Additionally, artificial intelligence (AI) is being used to develop algorithms to predict individual responses to foods and dietary patterns.
Findings from the MyFitnessApp and NIH studies can be married together to fine-tune the best overall approach to weight management and disease prevention. “Hopefully in the next 5 to 10 years we will have a breakthrough and be able to say for example, a patient should follow a ketogenic diet for 2 meals followed by a Mediterranean diet for the third meal,” Dr Li said. “That would be the ultimate goal.”
MyFitnessPal and UCLA partner to uncover the science of success: how and why users reach their health and fitness goals. News release. PRNewswire. March 14, 2023. Accessed April 17, 2023. https://www.prnewswire.com/news-releases/myfitnesspal-and-ucla-partner-to-uncover-the-science-of-success-how-and-why-users-reach-their-health-and-fitness-goals-301771480.html
Nutrition for Precision Health, powered by the All of Us Research Program. National Institutes of Health. Accessed April 17, 2023. https://commonfund.nih.gov/nutritionforprecisionhealth