1091 YOUTH’S BEDTIME REGULARITY MEDIATES THE ASSOCIATION OF DEPRESSION AND ANXIETY WITH NEGATIVE ATTENTION BIAS
Bibliographic record
Abstract
Attention biases towards negative information and sleep problems are core clinical features in anxiety and depressive disorders. We studied if sleep patterns, which were readily malleable, were associated with such biases among depressed and anxious individuals. A youth sample recruited from local universities (n=188, age ranged from 17–24) were administered the Structured Clinical Interview for DSM-IV disorders, (47% have a lifetime history of depressive and/or anxiety disorder). All participants wore an acti-watch and completed a sleep diary for 5 days and then completed an affective go/no-go task (AGNG) and other neurocognitive assessment on the 6th day. Their sleep difficulty (e.g. sleep latency), and regularity of sleep timing (e.g. standard deviation of 5-day bedtime) were coded as indicator of sleep-wake behaviors with the discrimination index (d’) in the AGNG as indicator of attention bias. Depressed and anxious individuals had a lower d’ in the AGNG, F(1,176)=6.280, p=.013, ŋ2=.038 than healthy participants. There was a significant correlation between d’ on the AGNG with regularity of bedtime, r(168)=.225, p=.024. Structural equation model with depression and anxiety as predictor, bedtime regularity as mediator and d’ on AGNG as dependent variable achieved a very good fit, CFI=1.000, RMSEA<0.001, SRNR<.001. There was no significant direct effect (B=.032, Standard Error=.211, p=.880) but a significant indirect effect of depression and anxiety status in predicting d’ on AGNG through its effect on bedtime regularity. B=.180, Standard Error=.127, p=.031. Our findings support the mediating role of sleep of the associations between depressive and anxiety disorder with attention bias. Given the high prevalence of depressive and anxiety disorders and sleep problems among youth and the prevalence of attention bias in psychopathology, further longitudinal studies are warranted in investigating the causal interrelationships among these variables. Funder: General Research Fund (#18619616), Research Grant Council, HKSAR
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".