Night Screen Time is Associated with Cognitive Function in Healthy Young Adults: A Cross-Sectional Study
Bibliographic record
Abstract
Background: In recent years, a significant shift toward remote work, virtual education, and social distancing measures was witnessed, thereby leading people to increasingly depend on digital devices for communication, work, and entertainment. This increased exposure to screens has raised concerns regarding its potential impact on cognitive function. Purpose: This study investigated the relationship between screen time and cognitive function among healthy young adults. Methods: One hundred forty-five healthy individuals (mean age 21.55 ± 2.84 years) participated in this cross-sectional study. Sociodemographic information including age, sex, height, weight, and level of education were obtained. Participants reported screen time using a screen time questionnaire. Cognitive function tests including, Paced Auditory Serial Addition Task (PASAT), Montreal Cognitive Assessment (MoCA) and Symbol Digit Modalities Test (SDMT) were conducted. Multilinear regression analyses were used to examine the associations between age, sex, level of education, screen time, and cognitive function. Results: One hundred thirty-nine participants (76 women) completed the study. Increased night screen time, bachelor's educational level, and women were associated with lower PASAT scores (R2=0.258; p≤0.047). Moreover, increased night screen time was associated with lower MoCA scores (R2=0.029; p=0.035). However, no associations were found between night screen time and SDMT scores. Conclusion: Participants who had higher night screen exposure had lower cognitive scores in the information speed processing, working memory, calculation, and attention domains. Considering these findings, this study emphasizes on the importance of setting a future recommended screen time guidelines for young adults as well as to promote healthy cognitive habits in order to preserve cognitive function and reduce the risk of developing neurodegenerative disease in the future. Future prospective cohort studies involving a more diverse age range is needed.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".