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Record W4403173740 · doi:10.55489/njcm.151020244192

Restriction of Mobile Phone Usage at Bed Time: Effect on Sleep Quality, Mood and Cognitive Function

2024· article· en· W4403173740 on OpenAlexaboutno aff
Priyadharshini Sivagurunathan, Sasi Vaithilingan, R. Vinothkumar

Bibliographic record

VenueNational Journal of Community Medicine · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsMoodSleep qualityMobile phoneCognitionSleep (system call)Quality (philosophy)PsychologyFunction (biology)AudiologyClinical psychologyMedicineComputer scienceTelecommunicationsPsychiatry

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.398
Teacher spread0.362 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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