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Record W2792726137 · doi:10.2478/rjr-2018-0006

The effect of smartphone usage on the upper extremity performance among Saudi Youth, KSA

2018· article· en· W2792726137 on OpenAlexaboutno aff
Mohammed Khalid Alruzayhi, Muath Salman Almuhaini, Akrm Ibrahem Alwassel, Osama Mansour Alateeq

Bibliographic record

VenueRomanian Journal of Rhinology · 2018
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical therapyMedicineElbow flexionInternal rotationPhysical medicine and rehabilitationRange of motionExternal rotationMcGill Pain QuestionnaireElbowSmartphone addictionAddictionSurgeryVisual analogue scaleEngineering

Abstract

fetched live from OpenAlex

Abstract The current study aims to investigate the effect of smartphone usage on the upper extremity performance among Saudi youth. A goniometer to measure the Range of Motion (ROM), the Smartphone Addiction Scale (SAS), McGill Pain scale and Chattanooga stabilizer were used to perform the current study on a sample of 300 university students from Al-Imam Mohammed Bin Saud University. The results have shown that smartphone addiction is negatively correlated to the elbow flexion, shoulder flexion, shoulder extension, shoulder abduction, shoulder adduction, and both shoulder internal and external rotation. Furthermore, the results have shown that McGill pain scores were positively correlated to elbow flexion, shoulder flexion, shoulder extension, shoulder abduction, shoulder adduction, and both shoulder internal and external rotation. The study has concluded that smartphone usage among Saudi youth negatively affects the upper extremity and causes a significant increase in the pain intensity. The study has recommended that there is an urgent need for a significant awareness campaign to warn the community regarding the impact of using smartphones for long periods of time.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.011
GPT teacher head0.248
Teacher spread0.238 · 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

Citations16
Published2018
Admission routes1
Has abstractyes

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