Mol Psychiatry. 2025 Aug 27. doi: 10.1038/s41380-025-03188-6. Online ahead of print.
ABSTRACT
App-based interventions are scalable and effective in reducing anxiety in young adults. Still, two-thirds of young adults remain symptomatic. Uncovering the heterogeneity in young adult anxiety by identifying clinical subtypes can guide precision interventions that match individuals’ profiles. We aimed to (a) identify subtypes of young adult anxiety; (b) examine whether subtype membership predicted response to Maya – a self-guided cognitive behavioral therapy (CBT) app for young adult anxiety and depression. Fifty-eight young adults, aged 18-25, with anxiety disorder received Maya. We applied a bipartite network algorithm to identify subtypes based on baseline clinical characteristics. We conducted mixed-effects models to examine whether subtype membership predicted reduction in anxiety and comorbid depression severity. We identified 3 subtypes: (1) individuals with social anxiety and physical consequences of anxiety, and poor coping skills; (2) individuals with poor quality of life, anhedonia, low positive affect, and low distress tolerance; (3) individuals with poor sleep, high negative affect, anxiety, and depression severity. All subtypes showed a reduction in anxiety and depression severity. Subtype 3 showed the steepest reduction in anxiety and depression severity, followed by Subtype 2 and Subtype 1. In conclusion, using bipartite network, we identified distinct clinical subtypes that could explain the heterogeneity in anxiety in young adults. The app was efficacious in reducing anxiety and depression severity for all subtypes. Individuals who present with poor sleep, high negative affect and anxiety severity may be optimal candidates for a self-guided CBT app. Our results could guide development of personalized digital interventions to maximize efficacy.
PMID:40866542 | DOI:10.1038/s41380-025-03188-6
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