# Eating-Related Self-Regulation and Its Neural Substrates as Mechanisms Underlying the Sleep/Eating Behavior Association in Children with Overweight/Obesity: An Ecological Momentary Assessment Study

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 · $528,483

## Abstract

PROJECT SUMMARY/ABSTRACT
Insufficient sleep and excess weight status contribute to adverse health outcomes across the lifespan,
including risk for cardiometabolic disease. Cross-sectional data suggest that children with overweight/obesity
are more likely to experience sleep disturbances than their non-overweight peers. Although the nature of this
association may be bidirectional, prospective studies indicate that sleep impacts body weight regulation
through multiple physiological and psychological pathways. In particular, insufficient sleep is related to greater
energy intake and reduced diet quality in children. Although mechanisms explaining the association between
sleep and eating behavior are poorly understood, sleep restriction has been found to impact brain processes
related to reward valuation of food and self-regulation, the behavioral manifestations of which may increase
susceptibility to suboptimal dietary behaviors and subsequent weight gain. A limitation of prior research on
mechanisms is that much of it has been conducted in adults and in laboratory settings, thereby calling into
question the ecological validity of the findings. Alternatively, studies on sleep restriction/extension in children’s
natural environments have relied on retrospective reporting of eating behavior, included children across the
weight spectrum, and had limited focus on underlying mechanisms, particularly neural substrates. A clearer
understanding of momentary mechanisms involved in the sleep/eating association could improve development
and/or refinement of sleep-related interventions, particularly those delivered in real time when risk for engaging
in maladaptive eating is highest. The proposed R01 study will examine prospective associations among sleep,
eating-related self-regulation, and eating behavior in the natural environment. Community-based children with
overweight or obesity (n=120) will undergo a naturalistic protocol involving assessment of typical sleep and
eating patterns (week 1), followed by sleep restriction or extension (weeks 2 and 3, separated by a 7-day
wash-out), the latter occurring within a randomized crossover design. Assessment throughout the study period
will involve daily actigraphy measurement of sleep patterns; repeated daily self-reports on eating behavior and
behavioral assessment of eating-related self-regulation; and intermittent 24-hour dietary recalls informed by
daily real-time food photography. Participants will complete fMRI-based assessment of neural activation during
an eating-related self-regulation task after each week-long period of sleep restriction and extension. Overall
aims are to assess short-term effects of sleep extension versus restriction on eating-related self-regulation
(including behavioral and neural performance) and naturalistic eating behavior. These data will clarify timing
and trajectory of changes in eating behavior and self-regulatory mechanisms as a consequence of sleep
patterns. The proposed stud...

## Key facts

- **NIH application ID:** 10401892
- **Project number:** 5R01HL147914-05
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Andrea Beth Goldschmidt
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $528,483
- **Award type:** 5
- **Project period:** 2019-08-15 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10401892

## Citation

> US National Institutes of Health, RePORTER application 10401892, Eating-Related Self-Regulation and Its Neural Substrates as Mechanisms Underlying the Sleep/Eating Behavior Association in Children with Overweight/Obesity: An Ecological Momentary Assessment Study (5R01HL147914-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10401892. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*