# Optimizing Just-in-Time Adaptive Intervention to Improve Dietary Adherence in Behavioral Obesity Treatment: A Micro-randomized Trial

> **NIH NIH R01** · MIRIAM HOSPITAL · 2022 · $620,820

## Abstract

PROJECT SUMMARY/ABSTRACT
Behavioral obesity treatment produces clinically significant weight loss and reduced disease risk/severity for
many individuals with overweight/obesity and cardiovascular disease. Yet, about half of patients fall short of
expected outcomes, which can be largely attributed to lapses from the recommended diet. Our work has
shown that dietary lapses (specific instances of nonadherence to dietary goals) are frequent during weight loss
attempts (~3-4 times per week), associated with poorer weight losses, and triggered by momentary changing
states (e.g., changes in mood or availability of palatable food). Thus, there is a clear need for innovative
solutions that can provide dynamic in-the-moment interventions to improve adherence to the prescribed diet in
obesity treatment. Our research team was the first to develop a smartphone-based just-in-time adaptive
intervention (JITAI) that includes: 1) daily ecological momentary assessment (EMA; repeated sampling via
mobile device) of relevant behavioral, psychological, and environmental triggers for lapse; 2) a machine
learning algorithm that uses information gathered via EMA to determine real-time lapse risk; & 3) delivery of
brief intervention during high-risk moments. Our pilot work revealed that the JITAI was feasible, acceptable,
and produced reductions in average lapse frequency. However, we have not yet shown a direct effect of the
JITAI on eating behavior in the moment of heightened lapse risk and know little about the types of interventions
that are most effective for reducing lapse. We therefore propose to extend our research via a micro-
randomized trial (MRT), a methodology that involves random assignment to intervention (or control) at a
specific decision point, i.e., when our algorithm predicts heightened risk for a lapse. The MRT will determine
whether a specific intervention in a specific moment had its intended effect. We will therefore port our JITAI to
a more scalable online platform and conduct a MRT to evaluate the effects of a generic lapse risk alert
message and theory-driven just-in-time interventions on dietary lapses. After refinement testing with n=15 to
ensure proper technical functioning of our updated JITAI, adults with overweight/obesity (n=159) will participate
in a well-established 12-week online obesity treatment program + JITAI, with 12 weeks of JITAI-only follow-up.
When an individual is at risk for lapsing s/he will be randomized to no intervention, a generic risk alert, or one
of 4 theory-driven interventions with interactive skills training. The outcome of interest will be the occurrence
(or lack thereof) of dietary lapse, as measured both subjectively (i.e., via EMA) and objectively (i.e., via wrist-
based intake monitoring), in the hours following randomization. Results of the MRT will inform an optimized
algorithm for intervention delivery that will drive the finalized JITAI. A future RCT will compare weight loss in
obesity treatment with and ...

## Key facts

- **NIH application ID:** 10427366
- **Project number:** 5R01HL153543-03
- **Recipient organization:** MIRIAM HOSPITAL
- **Principal Investigator:** Stephanie Paige Goldstein
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $620,820
- **Award type:** 5
- **Project period:** 2020-08-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10427366

## Citation

> US National Institutes of Health, RePORTER application 10427366, Optimizing Just-in-Time Adaptive Intervention to Improve Dietary Adherence in Behavioral Obesity Treatment: A Micro-randomized Trial (5R01HL153543-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10427366. Licensed CC0.

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