An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms

Linardon, Jake, Fuller-Tyszkiewicz, Matthew, Shatte, Adrian, and Greenwood, Christopher J. (2022) An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms. International Journal of Eating Disorders, 55 (6). pp. 845-850.

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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 (R2 = .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.

Item ID: 81627
Item Type: Article (Research - C1)
ISSN: 1098-108X
Keywords: eating disorders;digital interventions;machine learning
Copyright Information: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. © 2022 The Authors. International Journal of Eating Disorders published by Wiley Periodicals LLC.
Funders: National Health & Medical Research Council (NHMRC)
Projects and Grants: NHMRC APP1196948
Date Deposited: 23 Jan 2024 01:24
FoR Codes: 52 PSYCHOLOGY > 5203 Clinical and health psychology > 520399 Clinical and health psychology not elsewhere classified @ 30%
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified @ 70%
SEO Codes: 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280112 Expanding knowledge in the health sciences @ 40%
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220402 Applied computing @ 60%
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