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

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.