# Control Systems Engineering to Address the Problem of Weight Loss Maintenance: A System Identification Experiment to Model Behavioral & Psychosocial Factors Measured by Ecological Momentary Assessment

> **NIH NIH R01** · MIRIAM HOSPITAL · 2024 · $598,366

## Abstract

PROJECT SUMMARY/ABSTRACT
The most major and critical barrier to the treatment of obesity and comorbid conditions is weight loss
maintenance. A range of established treatments reliably produce clinically significant initial weight losses of 3-
30% of body weight, which substantially reduce risk and severity of disease, even when the weight loss is
modest. However, weight loss maintenance is uniformly poor, with most patients regaining at least some
weight and behaviorally treated patients returning to baseline weight within 5 years, thereby renewing risk for
weight-related illness. While regain is common, it is difficult to predict when or why an individual will begin to
regain lost weight. We therefore propose an experiment that will enable a future just-in-time adaptive
intervention (JITAI) that passively monitors triggers for lapse, identifies which triggers are most likely to
contribute to lapse for each patient, accurately predicts when a patient is entering a period of heightened risk
for lapse, determines the type(s) of intervention(s) that are likely to prevent the lapse, administers intervention
for as long as needed to reestablish healthy behavioral patterns for weight maintenance, and then returns to
passive monitoring. This automated intervention is nearly within reach via a combination of mobile
technologies, analytics, and behavioral intervention techniques that our team has already established,
including: (a) an Ecological Momentary Assessment platform to measure daily weight and related behavioral
and psychosocial influences; (b) an analytic framework, Control Systems Engineering, capable of modeling
complex patterns of behavioral and psychosocial influences on weight, and determining how best to intervene
on this “system” to facilitate weight loss maintenance; and (c) a toolbox of empirically validated behavioral
intervention strategies known to be effective for addressing common causes of weight regain. This proposal
aims to support our multidisciplinary team in combining our areas of expertise and prior work to enable a
“system identification (ID) experiment” with N=120 participants who have recently lost ≥3% of initial body
weight in a 6-month behavioral obesity treatment (N=180 will undergo behavioral obesity treatment to produce
this sample), who will be studied over a 12-month maintenance period. The data will be used to validate and
refine a theoretical model of weight loss maintenance. During the study, 4 interventions from the behavioral
toolbox will be administered randomly, thus providing necessary data on: (a) how lapse triggers are related to
each other and weight, and (b) which interventions are effective for addressing which triggers, for whom, and
under what circumstances. The end result of this project will be a control systems algorithm that can predict
when, for whom, and how to intervene to prevent weight regain, and a mobile platform that can be used to
deliver JITAI in the next phase of research. This highly ...

## Key facts

- **NIH application ID:** 10923932
- **Project number:** 5R01DK137423-02
- **Recipient organization:** MIRIAM HOSPITAL
- **Principal Investigator:** Eric Hekler
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $598,366
- **Award type:** 5
- **Project period:** 2023-09-15 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10923932

## Citation

> US National Institutes of Health, RePORTER application 10923932, Control Systems Engineering to Address the Problem of Weight Loss Maintenance: A System Identification Experiment to Model Behavioral & Psychosocial Factors Measured by Ecological Momentary Assessment (5R01DK137423-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10923932. Licensed CC0.

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