# Optimization of a mHealth Behavioral Weight Loss Intervention for Young Adults

> **NIH NIH R01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2024 · $534,293

## Abstract

PROJECT ABSTRACT
Obesity has reached epidemic proportions in the United States and is recognized as a major cause of
morbidity and mortality. Young adults (18-35 years) are at particularly high risk for weight gain and obesity. In-
person behavioral interventions generally produce clinically significant weight losses; however, cost and
access limit their potential to reduce obesity at a population level. Although web-based interventions that mimic
the structure of weekly face-to-face treatment have proven a viable alternative treatment, weight losses are
generally smaller than in-person treatment. Exclusively mobile treatments have been less effective, producing
1-3 kgs over 6 months. Newer digital intervention approaches called “Just-in-Time Adaptive Interventions”
(JITAIs) promise to improve upon outcomes by offering adaptive, personalized feedback on behavior “when
needed” in “real time,” rather than on a fixed schedule. This “just-in-time,” or JIT, approach is made possible by
the emergence of low-cost and widely available digital health tools that allow for the collection of continually
updated health data. However, few studies have used JIT approaches in remotely delivered, fully scalable
weight loss interventions. Although JITAIs are a potentially transformative approach to delivering obesity
interventions, a major obstacle in their development is efficient selection of components and systematic design
of an optimized intervention package that produces clinically meaningful weight losses with a population-level
strategy. To solve this problem, we will use the Multiphase Optimization Strategy (MOST), an engineering-
inspired framework, and a highly efficient experimental design to identify which levels of 5 intervention
components contribute meaningfully to change in weight over 6 months among young adults with overweight
and obesity. All participants (n=608) will receive a core 6-month weight loss intervention that includes
evidence-based lessons, behavioral skills training, and daily weighing. With the goal of determining if greater
adaptation will lead to greater weight loss, we will randomize participants to standard versus more adaptive
options of 5 additional intervention components: 1) diet monitoring approach (standard vs. simplified), 2)
adaptive physical activity goals (weekly vs. daily), 3) decision points for message timing (fixed vs. adaptive), 4)
decision rules for message content (standard vs. adaptive), and 5) message choice (no vs. yes). Candidate
components have been carefully selected from empirical evidence, tested in our prior studies, or in our pilot
micro-randomized trial. Assessments will occur at 0, 3 and 6 months to accomplish the following specific aims:
1) Build an optimized JITAI consisting of the set of intervention components that yield the greatest
improvement in weight change among young adults at 6 months; 2) Conduct mediation analyses to test the
relationships between the intervention components and hypoth...

## Key facts

- **NIH application ID:** 10875617
- **Project number:** 5R01DK125779-05
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Deborah F. Tate
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $534,293
- **Award type:** 5
- **Project period:** 2020-07-10 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10875617

## Citation

> US National Institutes of Health, RePORTER application 10875617, Optimization of a mHealth Behavioral Weight Loss Intervention for Young Adults (5R01DK125779-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10875617. Licensed CC0.

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