ArticleThe International journal of eating disorders2022
An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms.
Article in The International journal of eating disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Building and Validating an Explainable Machine Learning Model for Predicting Health-Promoting Behaviors in Older Adults: A Multicenter Study.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Predicting individual differences in digital alcohol intervention effectiveness through multimodal data.NPJ digital medicine · 2026Article
- Applying Machine Learning to Predict Complex Clinical Course in Youth With Eating Disorders.The International journal of eating disorders · 2026Article
- How Engagement Changes Over Time in a Digital Eating Disorder App: Observational Study.JMIR mHealth and uHealth · 2025Observational
- The promise and challenges of computer mouse trajectories in DMHIs - A feasibility study on pre-treatment dropout predictions.Internet interventions · 2025Article
- Current Practices and Perspectives of Artificial Intelligence in the Clinical Management of Eating Disorders: Insights From Clinicians and Community Participants.The International journal of eating disorders · 2025Article
- Using Machine Learning to Predict Uptake to an Online Self-Guided Intervention for Stress During the COVID-19 Pandemic.Stress and health : journal of the International Society for the Investigation of Stress · 2025Article
- Estimation of minimal data sets sizes for machine learning predictions in digital mental health interventions.NPJ digital medicine · 2024Article
- Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials.Journal of medical Internet research · 2023Article
- An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms.The International journal of eating disorders · 2022Article
- Dataset size versus homogeneity: A machine learning study on pooling intervention data in e-mental health dropout predictions.Digital healthArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveDigital interventions show promise to address eating disorder (ED) symptoms. However, response rates are variable, and the ability to predict responsiveness to digital interventions has been poor. We tested whether machine learning (ML) techniques can enhance outcome predictions from digital interventions for ED symptoms.
methodData were aggregated from three RCTs (n = 826) of self-guided digital interventions for EDs. Predictive models were developed for four key outcomes: uptake, adherence, drop-out, and symptom-level change. Seven ML techniques for classification were tested and compared against the generalized linear model (GLM).
resultsThe seven ML methods used to predict outcomes from 36 baseline variables were poor for the three engagement outcomes (AUCs = 0.48-0.52), but adequate for symptom-level change (R
conclusionA limited set of routinely measured baseline variables was not sufficient to detect a performance benefit of ML over traditional approaches. The benefits of ML may emerge when numerous usage pattern variables are modeled, although this validation in larger datasets before stronger conclusions can be made.
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