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Subjective and objective sleep and circadian parameters as predictors of depression-related outcomes: A machine learning approach in UK Biobank by Laura M. Lyall; Natasha Sangha; Xingxing Zhu; Donald M. Lyall; Joey Ward; Rona J. Strawbridge; Breda Cullen; Daniel J. Smith is a Psychology article available to read on EtoBox.

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Background Sleep and circadian disruption are associated with depression onset and severity, but it is unclear which features (e.g., sleep duration, chronotype) are important and whether they can identify individuals showing poorer outcomes. Methods Within a subset of the UK Biobank with actigraphy and mental health data (n = 64,353), penalised regression identified the most useful of 51 sleep/rest-activity predictors of depression-related outcomes; including case-control (Major Depression (MD) vs. controls; postnatal depression vs. controls) and within-case comparisons (severe vs. moderate MD; early vs. later onset, atypical vs. typical symptoms; comorbid anxiety; suicidality). Best models (of lasso, ridge, and elastic net) were selected based on Area Under the Curve (AUC). Results For MD vs. controls (n(MD) = 24,229; n(control) = 40,124), lasso AUC was 0.68, 95 % confidence interval (CI) 0.67–0.69. Discrimination was reasonable for atypical vs. typical symptoms (n(atypical) = 958; n(typical) = 18,722; ridge: AUC 0.74, 95 % CI 0.71–0.77) but poor for remaining models (AUCs 0.59–0.67). Key predictors across most models included: difficulty getting up, insomnia symptoms, snoring, ac

Who reads Subjective and objective sleep and circadian parameters as predictors of depression-related outcomes: A machine learning approach in UK Biobank?

Author

Laura M. Lyall; Natasha Sangha; Xingxing Zhu; Donald M. Lyall; Joey Ward; Rona J. Strawbridge; Breda Cullen; Daniel J. Smith

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Psychology (Social Sciences)