Bidirectional associations of sleep and discretionary screen time in adults: Longitudinal analysis of the UK biobank
Bibliographic record
Abstract
The direction of the association between discretionary screen time (DST) and sleep in the adult population is largely unknown. We examined the bidirectional associations of DST and sleep patterns in a longitudinal sample of adults in the general population. A total of 31,361 UK Biobank study participants (52% female, 56.1 ± 7.5 years) had two repeated measurements of discretionary screen time (TV viewing and leisure-time computer use) and self-reported sleep patterns (five sleep health characteristics) between 2012 and 2018 (follow-up period of 6.9 ± 2.2 years). We categorised daily DST into three groups (low, <3 h/day; medium, 3–4 h/day; and high, >4 h/day), and calculated a sleep pattern composite score comprising morning chronotype, adequate sleep duration (7–8 h/day), never or rare insomnia, never or rare snoring, and infrequent daytime sleepiness. The overall sleep pattern was categorised into three groups (healthy: ≥ 4; intermediate: 2–3; and poor: ≤ 1 healthy sleep characteristic). Multiple logistic regression analyses were applied to assess associations between DST and sleep with adjustments for potential confounders. Participants with either an intermediate (OR: 1.40; 95% CI: 1.15, 1.71) or a poor (OR: 1.16; 95% CI: 1.10, 1.24) sleep pattern at baseline showed higher odds for high DST at follow-up, compared with those with a healthy baseline sleep pattern. Participants with medium (OR: 1.40; 95% CI: 1.14, 1.71) or high DST (OR: 1.62; 95% CI: 1.30, 2.00) at baseline showed higher odds for poor sleep at follow-up, compared with participants with a low DST. In conclusion, our findings provide consistent evidence that a high DST at baseline is associated with poor sleep over a nearly 7 year follow-up period, and vice versa.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".