Logo image
Back
Using Wearable Passive Sensing to Predict Binge Eating in Response to Negative Affect among Individuals with Transdiagnostic Binge Eating: Protocol for an Observational Study (Preprint)
Journal article   Open access   Peer reviewed

Using Wearable Passive Sensing to Predict Binge Eating in Response to Negative Affect among Individuals with Transdiagnostic Binge Eating: Protocol for an Observational Study (Preprint)

Emily Presseller, Elizabeth W. Lampe, Fengqing Zhang, Philip A. Gable, Timothy C. Guetterman, Evan M. Forman and Adrienne S. Juarascio
JMIR research protocols
07 Mar 2023
DOI: https://doi.org/10.2196/47098
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.2196/47098View
Accepted (AM)CC BY V4.0,  Open

Abstract

Binge eating (BE), characterized by eating a large amount of food accompanied by a sense of loss of control over eating, is a public health crisis. Negative affect is a well-established antecedent for BE. The affect regulation model of BE posits that elevated negative affect increases momentary risk for BE, as engaging in BE alleviates negative affect and reinforces the behavior. The eating disorder field’s capacity to identify moments of elevated negative affect, and thus BE risk, has exclusively relied on ecological momentary assessment (EMA). EMA involves the completion of surveys in real time on one’s smartphone to report behavioral, cognitive, and emotional symptoms throughout the day. Although EMA provides ecologically valid information, EMA surveys are often delivered only 5-6 times per day, involve self-report of affect intensity only, and are unable to assess affect-related physiological arousal. Wearable, psychophysiological sensors that measure markers of affect arousal including heart rate, heart rate variability, and electrodermal activity, may augment EMA surveys to improve accurate real-time prediction of BE. These sensors can objectively and continuously measure biomarkers of nervous system arousal that coincide with affect, thus allowing them to measure affective trajectories on a continuous timescale, detect changes in negative affect before the individual is consciously aware of them, and reduce user burden to improve data completeness. However, it is unknown whether sensor features can distinguish between positive and negative affect states, given that physiological arousal may occur during both negative and positive affect states.

Metrics

9 Record Views
6 citations in Web of Science
4 citations in Scopus

Details

UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being
#5 Gender Equality

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Collaboration types
Domestic collaboration
Web of Science research areas
Health Care Sciences & Services
Public, Environmental & Occupational Health
Logo image