# SenseWhy: Overeating in Obesity Through the Lens of Passive Sensing.

> **NIH NIH K25** · NORTHWESTERN UNIVERSITY · 2021 · $53,790

## Abstract

PROJECT SUMMARY/ABSTRACT
Medical professionals have recently put to rest the idea that there is an ideal weight loss diet for
everyone. One cause for obesity is overeating, but we do not know what patterns and behaviors
contribute to this problematic habit. Defining problematic eating behaviors that lead to energy
imbalance is essential for treating obesity. Studies typically focus on a single putative causal
mechanism of overeating such as stress or craving, not addressing the multiple features that co-
occur with overeating. Hence, the factors that predict overeating episodes remain unknown, as
do which of them contribute to an individual's consistency and variability of overeating.
Given recent advancements in passive sensing, we now have the potential to detect problematic
eating using seamlessly captured physiological features such as number of feeding gestures and
swallows, and heart rate variability. Collecting detectable and predictable features that identify
overeating will hone in on the patterns that interventionists may optimally target to help
populations with obesity understand their eating habits and ultimately improve their ability to self-
regulate their eating behaviors. Location-scale models will map the factors that most contribute
to habit formation within subjects, providing interventionists with essential targets to guide
behavior.
The first aim is to collect sensor-based and ecological momentary assessment data (to assess
factors not yet detectable through sensing) from adults with obesity and apply machine learning
algorithms to identify a subset of features that detect overeating, as validated against ground truth
of videotaped eating episodes and 24 hour dietary recall. Participants will wear a passive sensing
sensor suite and respond to random and event-triggered prompts regarding each eating episode.
Then, machine learning will determine the optimal feature subset that detect overeating episodes
using Gradient Boosting Machines. In the second aim, hierarchical clustering techniques will
cluster overeating episodes into theoretically meaningful and clinically known problematic
behaviors related to overeating. The final aim is to build statistical models that explain the effect
of detectable and clinically-known problematic features on new habit formation. These models will
lay a foundation for optimization studies to discover evidence-based decision rules that can guide
timely interventions to treat obesity by preventing overeating, and maintaining healthy eating
behaviors.

## Key facts

- **NIH application ID:** 10406434
- **Project number:** 3K25DK113242-04S1
- **Recipient organization:** NORTHWESTERN UNIVERSITY
- **Principal Investigator:** Nabil Alshurafa
- **Activity code:** K25 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $53,790
- **Award type:** 3
- **Project period:** 2018-01-01 → 2022-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10406434

## Citation

> US National Institutes of Health, RePORTER application 10406434, SenseWhy: Overeating in Obesity Through the Lens of Passive Sensing. (3K25DK113242-04S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10406434. Licensed CC0.

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