Problematic Smartphone Use: An Overlooked Trigger for Oculomotor Strain, Cervical Disability, and Headache in Medical Students
Bibliographic record
Abstract
Objectives: Problematic smartphone use (PSU) is widespread among medical students and might be associated with visual, musculoskeletal, and neurological complaints. In this study, we investigated the relationship between PSU and oculomotor strain, cervical disability, and the impact of headaches. Materials and Methods: The present study was conducted through a cross-sectional survey among 498 medical students in Lahore, Pakistan. PSU was assessed using the Smartphone Addiction Scale-Short Version (SAS-SV), oculomotor strain with the Computer Vision Syndrome Questionnaire (CVS-Q), cervical disability with the Neck Disability Index (NDI), and headache impact using the HIT-6. Data were assessed using Pearson's correlations, t-test, ANOVA, and multivariate regression in SPSS v.26. Results: The average SAS-SV score was 32.1 ± 4.9. PSU was associated with CVS (r = 0.229), NDI (r = 0.147), and HIT-6 (r = 0.088). Scores for all measures were higher among female students (p < 0.001). The youngest students presented with higher PSU; the CVS was more intense in 21-23 years of age, and older students had a greater cervical disability. Predictors of CVS, cervical disability, and headache impact were PSU, gender (female), younger age, low physical activity status, and comorbidities according to the regression models. Conclusion: PSU is associated with increased oculomotor strain, neck disability, and headache burden in medical students, especially for females, younger-aged students, and those having low activity or comorbidities. Preventive strategies that promote digital well-being and healthy behaviours are advised.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".