# Using Multimodal Real-Time Assessment to Phenotype Dietary Non-Adherence Behaviors that Contribute to Poor Outcomes in Behavioral Obesity Treatment

> **NIH NIH R01** · MIRIAM HOSPITAL · 2022 · $674,668

## Abstract

PROJECT SUMMARY/ABSTRACT
Behavioral obesity treatment (BOT) produces clinically significant weight loss and reduced disease risk/severity
for many individuals with overweight/obesity. Yet, many 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 the prescribed calorie target(s) in BOT) are frequent during weight loss attempts,
and are associated with poorer weight losses and higher daily energy intake. Despite the potential for lapses to
influence BOT outcomes and health, poorly understood variability in types of lapse behaviors and their
mechanisms interferes with our ability to intervene on them. In our research, participants have identified distinct
behaviors associated with lapse (e.g., eating an off-plan food, eating too large a portion of food). Across several
studies, we have established the concept of “dietary lapse types” (i.e., specific eating behavior(s) and contextual
factors underlying a dietary lapse). We have shown that behavioral, psychosocial, and contextual mechanisms
may differ across dietary lapse types, and that some lapse types appear to be more detrimental than others for
weight control. Elucidating clear dietary lapse types therefore has major potential for understanding and
improving adherence in BOT, but we have been unable to do so because our work is limited to secondary
analyses of data from larger trials that have incomplete measures of lapse types, potential mechanisms, and
clinical outcomes. We propose to extend our research by using behavioral phenotyping (i.e., data-driven
identification of underlying behavioral, psychological, and contextual factors of a health behavior) to establish
lapse phenotypes, and understand their impact on clinical outcomes. While typical phenotyping studies cluster
individuals via unique characteristics, we aim to understand phenotypes of lapses as a specific behavior within
individuals. We will use multimodal real-time assessment tools within a multi-level factor analysis framework to
uncover phenotypes while accounting for behaviors occurring within individuals and within days. Adults with
overweight/obesity (n=150) will participate in a well-established 12-mo. online BOT and 6-mo. weight loss
maintenance period. Participants will complete a 14-day lapse phenotyping assessment battery at baseline, 4,
8, 12 and 18 months. EMA and passive sensing tools (i.e., wrist devices, geolocation) will assess dietary lapses
and relevant phenotyping characteristics identified from our prior work. Participant energy intake will be assessed
with 24-hour dietary recalls and weight will be measured pre- and post- assessment. Results will yield a set of
lapse phenotypes and knowledge of their underlying mechanisms, which will can inform novel interventions to
improve dietary adherence in BOT (and in other treatments for which dietary adherence is critical). This
innov...

## Key facts

- **NIH application ID:** 10418847
- **Project number:** 1R01DK132210-01
- **Recipient organization:** MIRIAM HOSPITAL
- **Principal Investigator:** Stephanie Paige Goldstein
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $674,668
- **Award type:** 1
- **Project period:** 2022-05-01 → 2027-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10418847

## Citation

> US National Institutes of Health, RePORTER application 10418847, Using Multimodal Real-Time Assessment to Phenotype Dietary Non-Adherence Behaviors that Contribute to Poor Outcomes in Behavioral Obesity Treatment (1R01DK132210-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10418847. Licensed CC0.

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