ArticlePsychological medicine2024
Using machine learning with passive wearable sensors to pilot the detection of eating disorder behaviors in everyday life.
Article in Psychological medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence in the Management of Malnutrition in Cancer Patients: A Systematic Review.Advances in nutrition (Bethesda, Md.) · 2025Pooled it
- Momentary physiological indices related to eating disorders: A systematic and methodological review.European eating disorders review : the journal of the Eating Disorders Association · 2024Pooled it
- Perspectives on Remote Monitoring via Smartphones and Wearables Among Individuals With Lived Experience or at Risk of Eating Disorders ("This Could Go Very, Very Wrong"): Qualitative Interview Study.JMIR formative research · 2026Article
- AI and machine learning for early identification of eating disorders: a narrative review and Indian contextual insights.Eating and weight disorders : EWD · 2026Review
- Integrating Artificial Intelligence and Smartphone Technology to Enhance Personalized Assessment and Treatment for Eating Disorders.The International journal of eating disorders · 2025Review
- Personalised and precision mental health in eating disorders: why routine outcome measurement is key.Journal of eating disorders · 2025Review
- Ecological momentary interventions for eating disorders: a systematic review of the nascent science and recommendations for future research.Journal of eating disorders · 2025Article
- Feature Selection for Physical Activity Prediction Using Ecological Momentary Assessments to Personalize Intervention Timing: Longitudinal Observational Study.JMIR mHealth and uHealth · 2025Observational
- What's in your card? The impact of online food delivery apps on depression and eating behaviors.Frontiers in nutrition · 2025Article
- Differentiation between atypical anorexia nervosa and anorexia nervosa using machine learning.The International journal of eating disorders · 2024Article
- Bioinformed idiographic symptom networks to identify neurobiological predictors of affective-state transitions in bipolar disorder.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
backgroundEating 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.
methodsFrom an ongoing study (Final
resultsUsing physiological data, ML LRC accurately classified on average 91% of cases, with 92% specificity and 90% sensitivity.
conclusionsThis 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.