P.085 The relationship between sleep and behavior in attention deficit/hyperactivity disorder
Bibliographic record
Abstract
Background: Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that is associated with long-term reduced quality of life and impaired functioning. ADHD is commonly associated with sleep disturbances that can contribute to many difficulties in a child’s life. This study aims to elucidate this complex relationship by utilizing a subset of the Adolescent Brain Cognitive Development (ABCD) database. Methods: The population included a group of children with ADHD age 10-13 years (n=212) and a matched typically developing (TD) group (n=212). Sleep data was obtained through Fitbit actigraphy measures, and the Parent Sleep Disturbance Scale (SDS). Behavioural and emotional subscores were obtained from the Child Behaviour Checklist (CBCL). Results: There were no significant correlations between the actigraphy and SDS sleep data. SDS sleep data were significantly different between ADHD and control groups, while actigraphy data was not. Sleep latency (measured by actigraphy) and 3 out of 6 of the SDS subscores were significantly related to behavioural scores. Conclusions: The results of this study indicate that sleep may not be an important mediator of behaviour and emotional responses in children with ADHD. Future studies should explore both influences on sleep parameters as well as behaviour and other measures important to families.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".