Machine learning analysis reveals distinct engagement subtypes in digital mental health interventions for depression and anxiety, highlighting pathways to tailor care and improve clinical outcomes.
The findings of this study may facilitate tailoring interventions according to specific subtypes of engagement for individuals with depression and anxiety. Informing clinical decision needs of supporters may be a route to successful adoption of machine learning insights, thus improving clinical outcomes overall.
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Chien et al. (2020) studied this question.
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