# Predicting Binge and Purge Episodes from Passive and Active Apple Watch Data Using a Dynamical Systems Approach

> **NIH NIH R01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2021 · $706,023

## Abstract

PROJECT SUMMARY/ABSTRACT
Bulimia nervosa (BN) and binge eating disorder (BED) are life-interrupting and associated with significant
impairment. Via a unique opportunity that allowed us to adapt the widely used cognitive-behavioral based app
Recovery Record for use on 1000 Apple Watches, we propose to optimize two domains of data being
collected over a 30-day period in 1000 individuals with bulimia nervosa (BN) or binge-eating disorder (BED).
This proposal augments a parent study [Binge Eating Genetics INitiative (BEGIN)], supported by NIMH (saliva
kits for DNA at no cost). We will collect longitudinal passive sensor data via native applications in the Apple
Watch and active data on binge-eating, purging, nutrition, mood, and cognitions using Recovery Record
adapted for the Apple Watch. We will combine sensor-based measurements of autonomic nervous system
(ANS) activity, actigraphy, and geolocation with active Recovery Record measures to characterize real world
conditions under which individuals are more/less likely to binge and/or purge in their daily lives. Applying
dynamical systems analytic approaches, both across and within individuals, we will identify stable, low-risk,
and high-risk patterns that will enable the prediction of transition to high risk epochs that signal impending
binge or purge episodes. Our work will provide an empirical foundation for transcending current cognitive-
behavioral therapy approaches that are dependent on self-report (often retrospective) of high risk states, will
enhance the understanding of eating disorders in terms of regulation, and will yield a personalized precision
medicine approach to eating disorders treatment. Efficient and reliable quantitative characterization is the
essential first step in the development of real-time interventions driven by automated recognition of
individualized transitions into high-risk periods for disordered eating behaviors. Our aims are: 1) To predict the
occurrence of binge eating and purging episodes in individuals with BN or BED with passive sensor data; 2)
To test theoretically-derived regulatory models of binge eating and purging as reflected in differences in
temporal patterns; and 3) To refine our capacity to predict high risk states by augmenting passive data with
contextual factors collected by Recovery Record. This proposal optimizes the richness and longitudinal
structure of the deep phenotypic data collected in BEGIN to lay the foundation for the next translational step in
which we will develop personalized just-in-time interventions that can disrupt eating disorders behaviors in
real time before they occur.

## Key facts

- **NIH application ID:** 10215486
- **Project number:** 5R01MH119084-03
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** CYNTHIA M BULIK
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $706,023
- **Award type:** 5
- **Project period:** 2019-09-23 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10215486

## Citation

> US National Institutes of Health, RePORTER application 10215486, Predicting Binge and Purge Episodes from Passive and Active Apple Watch Data Using a Dynamical Systems Approach (5R01MH119084-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10215486. Licensed CC0.

---

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