An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms. Issue 6 (12th May 2022)

• Record Type: Journal Article
• Authors: Linardon, Jake; Fuller‐Tyszkiewicz, Matthew; Shatte, Adrian; Greenwood, Christopher J.
• Is Part Of: International journal of eating disorders. Volume 55:Issue 6(2022)
• Page Start: 845
• Page End: 850
• Publication Date: 2022-05-12
• Subjects: adherence -- digital -- eating disorders -- e‐health -- engagement -- intervention -- machine learning -- prediction -- randomized controlled trial -- uptake; Appetite disorders -- Periodicals; Ingestion disorders -- Periodicals; Eating disorders -- Periodicals; 616.8526
• DOI: 10.1002/eat.23733
• Languages: English
• ISSNs: 0276-3478

Abstract

Abstract: Objective: Digital 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. Method: Data 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). Results: The 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 2 = .15–.40). ML did not offer an added benefit to the GLM. Incorporating intervention usage pattern data improved ML prediction accuracy for drop‐out (AUC = 0.75–0.93) and adherence (AUC = 0.92–0.99). Age, motivation, symptom severity, and anxiety emerged as influential outcome predictors. Conclusion: A 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.