Associations Between Preschooler Screen Time Trajectories and Executive Function
Bibliographic record
Abstract
OBJECTIVE: To examine associations between preschooler screen time trajectories and executive functions and effortful control at age 5. METHODS: Prospective, community-based convenience sample of 315 parents of preschoolers (54% male), studied at the ages of 3.5 (2020), 4.5 (2021), and 5.5 (2022). Parent-reported screen use at the ages of 3.5, 4.5, and 5.5 was used to estimate preschooler screen use trajectories. Using latent growth modeling, we identified low (mean=.9h/d, 23%), medium (mean=3.0h/d, 56%), and high (mean=6.38h/d, 21%) screen time groups. Children completed assessments of inhibitory control and cognitive flexibility at age 5.5. Both tasks are from the National Institute of Health Toolbox. Parents reported child effortful control at the age of 3.5 and 5.5 using the Children's Behavior Questionnaire, educational attainment, and parenting stress. RESULTS: Children in the average (b=-5.24) and high (b=.9.30) screen time trajectories scored significantly lower on inhibitory control than those in the low screen time group. Children in the average and high screen time groups also scored higher than children in the low screen time group on cognitive flexibility (b=-4.50) and (b=-10.12), respectively. Finally, children in the average and high screen time groups scored lower than children in the low screen time groups on effortful control (b=-.41) and (b=-.61), respectively. CONCLUSIONS: The present study shows that stability in high levels of screen use is common among preschoolers and may forecast higher risk of cognitive difficulty and lower levels of cognitive control by the time of school entry. SUMMARY: High levels of preschooler screen use were associated with lower scores on assessments of inhibitory control, cognitive flexibility, and effortful control.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".