Bedtime screen use behaviors and sleep outcomes: Findings from the Adolescent Brain Cognitive Development (ABCD) Study
Bibliographic record
Abstract
OBJECTIVES: To determine associations between bedtime screen time behaviors and sleep outcomes in a national study of early adolescents. METHODS: We analyzed cross-sectional data from 10,280 early adolescents aged 10-14 (48.8% female) in the Adolescent Brain Cognitive Development Study (Year 2, 2018-2020). Regression analyses examined the association between self-reported bedtime screen use and self- and caregiver-reported sleep measures, including sleep disturbance symptoms, controlling for sex, race/ethnicity, household income, parent education, depression, data collection period (pre- vs. during COVID-19 pandemic), and study site. RESULTS: Overall, 16% of adolescents had at least some trouble falling or staying asleep in the past 2 weeks and 28% had overall sleep disturbance, based on caregiver reports. Adolescents who had a television or an Internet-connected electronic device in the bedroom had a greater risk of having trouble falling or staying asleep (adjusted risk ratio 1.27, 95% CI 1.12-1.44) and overall sleep disturbance (adjusted risk ratio 1.15, 95% CI 1.06-1.25). Adolescents who left their phone ringer activated overnight had more trouble falling/staying asleep and greater overall sleep disturbance compared to those who turned off their cell phones at bedtime. Streaming movies, playing video games, listening to music, talking/texting on the phone, and using social media or chat rooms were all associated with trouble falling/staying asleep and sleep disturbance. CONCLUSIONS: Several bedtime screen use behaviors are associated with sleep disturbances in early adolescents. The study's findings can inform guidance for specific bedtime screen behaviors among early adolescents.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".