Screen exposure, sleep quality, and language development in 6-month-old infants
Bibliographic record
Abstract
Introduction Screen time can have important ramifications for children's development and health. Children exposed to greater screen time score lower on assessments of language development and tend to sleep less. However, most studies examining associations among screen time, language development, and sleep quality have focused on older children and/or have relied on subjective assessments of screen time exposure (i.e., parent report). The current study examined whether screen exposure, assessed via both maternal-report questionnaires and in-home audio recordings, was associated with differences in language development and sleep quality in infants at ~6 months of age (N = 187). Methods Mothers completed questionnaires to assess infant screen exposure, language production, and sleep quality, as well as family socioeconomic and demographic factors. The Language Environment Analysis (LENA) recorder was used to measure home screen use and the language environment. Results Higher family income and higher maternal education were associated with less infant screen time, as assessed by both maternal report and in-home LENA recordings. Neither measure of infant screen exposure was significantly associated with the home language environment, maternally-reported infant language production, or infant sleep quality. Maternally-reported screen exposure showed a small but significant positive correlation with LENA-derived screen exposure. Discussion We find no detectable association between screen exposure and differences in maternally reported language development or sleep quality in the first 6 months of life. Future studies will be needed to examine associations among screen time and subsequent infant development and health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".