Restriction of Mobile Phone Usage at Bed Time: Effect on Sleep Quality, Mood and Cognitive Function
Bibliographic record
Abstract
Introduction: Mobile phones are integral to modern life, but excessive use, particularly at night, can lead to disrupt well-being. Limiting mobile phone use before bedtime may improve individual well-being. This study aimed to evaluate whether restricting mobile phone use at bedtime enhances sleep quality, mood, and cognitive function. Methodology: Undergraduate students from a selected engineering college were assessed for bedtime mobile phone use. Sixty-eight students were chosen via simple random sampling. A self-reported questionnaire including the Pittsburgh Sleep Quality Index, Positive and Negative Affect Scale, and Montreal Cognitive Assessment Scale evaluated sleep quality, mood, and cognitive function before implementing restrictions. The "Lock My Phone" app was used to enforce these restrictions. Post-intervention assessments were conducted on the 15th and 30th days. Results: Before the intervention, all students reported poor sleep quality, 80.8% had reduced positive affect, 91.1% experienced high negative affect, and only 23.5% had normal cognitive function. Significant improvements were observed in sleep quality, mood, and cognitive function post-restriction (p<0.001). Conclusion: Restricting mobile phone use before bedtime significantly improved sleep quality, mood, and cognitive function among undergraduate students.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".