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Using machine learning with passive wearable sensors to pilot the detection of eating disorder behaviors in everyday life

Published online by Cambridge University Press:  20 October 2023

C. Ralph-Nearman Open the ORCID record for C. Ralph-Nearman [Opens in a new window] ,
L. E. Sandoval-Araujo Open the ORCID record for L. E. Sandoval-Araujo [Opens in a new window] ,
A. Karem Open the ORCID record for A. Karem [Opens in a new window] ,
C. E. Cusack Open the ORCID record for C. E. Cusack [Opens in a new window] ,
S. Glatt ,
M. A. Hooper Open the ORCID record for M. A. Hooper [Opens in a new window] ,
C. Rodriguez Pena ,
D. Cohen ,
S. Allen  and
E. D. Cash Open the ORCID record for E. D. Cash [Opens in a new window]
...Show all authors
C. Ralph-Nearman*
Affiliation:
Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA
L. E. Sandoval-Araujo
Affiliation:
Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA
A. Karem
Affiliation:
Department of Computer Science and Engineering, University of Louisville, Louisville, KY, USA
C. E. Cusack
Affiliation:
Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA
S. Glatt
Affiliation:
Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA
M. A. Hooper
Affiliation:
Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA Department of Psychology, Vanderbilt University, Nashville, TN, USA
C. Rodriguez Pena
Affiliation:
Department of Computer Science and Engineering, University of Louisville, Louisville, KY, USA
D. Cohen
Affiliation:
Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA
S. Allen
Affiliation:
Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA
E. D. Cash
Affiliation:
Department of Otolaryngology-HNS and Communicative Disorders, University of Louisville School of Medicine, Louisville, KY, USA University of Louisville Healthcare-Brown Cancer Center, Louisville, KY, USA
K. Welch
Affiliation:
Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA
C. A. Levinson
Affiliation:
Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY, USA Department of Pediatrics, Child and Adolescent Psychology and Psychiatry, University of Louisville, Louisville, KY, USA
*
Corresponding author: C. Ralph-Nearman; Email: Christina.Ralph-Nearman@Louisville.edu; ChristinaRalphNearman@gmail.com
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Abstract

Background

Eating disorders (ED) are serious psychiatric disorders, taking a life every 52 minutes, with high relapse. There are currently no support or effective intervention therapeutics for individuals with an ED in their everyday life. The aim of this study is to build idiographic machine learning (ML) models to evaluate the performance of physiological recordings to detect individual ED behaviors in naturalistic settings.

Methods

From an ongoing study (Final N = 120), we piloted the ability for ML to detect an individual's ED behavioral episodes (e.g. purging) from physiological data in six individuals diagnosed with an ED, all of whom endorsed purging. Participants wore an ambulatory monitor for 30 days and tapped a button to denote ED behavioral episodes. We built idiographic (N = 1) logistic regression classifiers (LRC) ML trained models to identify onset of episodes (~600 windows) v. baseline (~571 windows) physiology (Heart Rate, Electrodermal Activity, and Temperature).

Results

Using physiological data, ML LRC accurately classified on average 91% of cases, with 92% specificity and 90% sensitivity.

Conclusions

This evidence suggests the ability to build idiographic ML models that detect ED behaviors from physiological indices within everyday life with a high level of accuracy. The novel use of ML with wearable sensors to detect physiological patterns of ED behavior pre-onset can lead to just-in-time clinical interventions to disrupt problematic behaviors and promote ED recovery.

Keywords

ambulatory assessmentbiomarkereating disordersjust-in-time interventionpersonalized medicinephysiologicalwearables
Type
Original Article
Information
Psychological Medicine , Volume 54 , Issue 6 , April 2024 , pp. 1084 - 1090
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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