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

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 bu

SUBMITTER: Ralph-Nearman C 

PROVIDER: S-EPMC10939805 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature