Impact of a behavioral intervention, delivered by pediatricians or psychologists, on sleep problems in children with ADHD: a cluster‐randomized, translational trial
Bibliographic record
Abstract
BACKGROUND: We have demonstrated the efficacy of a brief behavioral intervention for sleep in children with ADHD in a previous randomized controlled trial and now aim to examine whether this intervention is effective and cost-effective when delivered by pediatricians or psychologists in community settings. METHODS: Translational, cluster-randomized trial of a behavioral intervention versus usual care from 19th January, 2015 to 30th June, 2017. Participants (n = 361) were children aged 5-13 years with ADHD and parent report of a moderate/severe sleep problem who met criteria for American Academy of Sleep Medicine criteria for chronic insomnia disorder, delayed sleep-wake phase disorder, or were experiencing sleep-related anxiety. Participants were randomized at the level of the pediatrician (n = 61) to intervention (n = 183) or usual care (n = 178). Families in the intervention group received two consultations with a pediatrician or a psychologist covering sleep hygiene and tailored behavioral strategies. RESULTS: In an intention-to-treat analysis, at 3 and 6 months respectively, the proportion of children with moderate to severe sleep problems was lower in the intervention (28.0%, 35.8%) compared with usual care group (55.4%, 60.1%; 3 month: risk ratio (RR): 0.51, 95% CI 0.37, 0.70, p < .001; 6 month: RR: 0.58; 95% CI 0.45, 0.76, p < .001). Intervention children had improvements across multiple Children's Sleep Habits Questionnaire subscales at 3 and 6 months. No benefits of the intervention were observed in other domains. Cost-effectiveness of the intervention was AUD 13 per percentage point reduction in child sleep problem at 3 months. CONCLUSIONS: A low-cost brief behavioral sleep intervention is effective in improving sleep problems when delivered by community clinicians. Greater sample comorbidity, lower intervention dose or insufficient clinician supervisions may have contributed to the lack benefits seen in our previous trial.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.000 |
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 teacher head, 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".