ArticleJournal of anxiety disorders2026
Actigraphy and subjective sleep predictors of nine-year generalized anxiety disorder.
Article in Journal of anxiety disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundSleep disturbances have been linked to generalized anxiety disorder (GAD) symptoms. However, cross-sectional studies, linearity assumptions, and limited predictor sets preclude identifying which unique sleep disturbance markers precede GAD symptoms. We thus harnessed machine learning (ML) to determine objective and subjective sleep disturbance predictors of nine-year GAD symptoms.
methodsCommunity adults (N = 1054) underwent baseline surveys, clinical interviews, and seven-day sleep actigraphy protocols. GAD symptoms were reassessed nine years later. Seven ML models were examined with 44 baseline predictors. Partial dependence and Shapley additive explanation plots were created as interpretable ML approaches with the best-performing random forest model using nested cross-validation. Sensitivity analyses included and excluded GAD sleep items.
resultsThe final multivariable predictive algorithm performed well (R
conclusionsOutcomes highlight the importance of combining actigraphy and self-report sleep assessments. Future studies should determine the degree to which these patterns extend to the within-person level to develop early prevention, treatment, and precision mental health strategies for individuals at risk of, or with, increased GAD severity.
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