Optimizing Self-Monitoring Feedback Delivery for the Treatment of Overweight and ObesityPI: Ruiz (UF), Ross (Advocate Health), Krukowski (U of Virgina)

Funding Organization:
National Institute of Health
Funding Amount:
$3.4M
Funding Period:
2026-2031

Obesity remains one of the most pressing public health challenges in the United States, affecting more than 40% of adults and contributing to a variety of chronic health conditions. Although behavioral weight management programs have proven effective in clinical trials, their implementation in community settings often yields more modest results — typically around 4% weight loss compared to the 8 to 10% seen in controlled environments. A key barrier is the lack of consistent, personalized feedback on self-monitoring behaviors such as diet, physical activity and weight tracking.

This project seeks to bridge this gap by developing a semi-automated feedback system that can be integrated into a variety of community-based and clinical weight management programs. The system will provide tailored, data-driven feedback to participants, helping them stay engaged and on track with their health goals.

“We know that timely, personalized feedback can significantly improve the outcomes that we see in weight management programs,” said Ross. “But in many community settings, staff lack the time or training necessary to provide this feedback. Our goal is to create a system that combines the expertise of intervention staff with the efficiency of automation, making high-quality treatment more accessible to those who need it most.”

The research will be conducted in three phases:

  • The first phase involves the enrollment of 300 adults with overweight or obesity in a 16-week program. Participants will use Fitbit tools to self-monitor their behaviors and receive weekly feedback messages. A micro-randomized factorial trial design will be used to test the impact of different feedback components on weight loss and adherence to program goals. The aim of this trial is to develop an optimized algorithm for feedback provision, which will determine who should receive what kind of feedback, and in what context, enabling interventions to use a precision-medicine approach to feedback delivery.
  • The second phase will use user-centered design methods, including several cycles of development and testing, to develop and refine a semi-automated feedback system.
  • The third phase will evaluate the usability of this new system in a real-world setting, working with intervention facilitators to ensure the tool meets practical needs.

By optimizing how feedback is constructed and delivered, the project aims to personalize treatment, improve outcomes and expand access to effective obesity interventions.